Book

1. T. Akutsu, Algorithms for Analysis, Inference, and Control of Boolean Networks,
   World Scientific, 2018.

Journal Papers

1. T. Akutsu and A. A. Melkman, On the size and width of the decoder of a Boolean threshold autoencoder (Brief Paper),
   IEEE Transactions on Neural Networks and Learning Systems, in press.
2. W. Someya, T. Akutsu, J. M. Schwartz, and J. C. Nacher, Measuring criticality in control of complex biological networks,
   NPJ Systems Biolohy and Applications, 10(1), 9 (16 pages), 2024.
3. L. Mu, J. Song, T. Akutsu, and T. Mori, DiCleave: a deep learning model for predicting human Dicer cleavage sites,
   BMC Bioinformatics, 25(1), 13 (15 pages), 2024.
4. Y. Cui, Z. Wang, X. Wang, Y. Zhang, Y. Zhang, T. Pan, Z. Zhang, S. Li, Y. Guo, T. Akutsu, and J. Song, SMG: self-supervised masked graph learning for cancer gene identification (Problem Solving Protocol),
   Briefings in Bioinformatics, 24(6), bbad406 (13 pages), 2023.
5. K. Shiota K and T. Akutsu, Multi-shelled ECIF: improved extended connectivity interaction features for accurate binding affinity prediction,
   Bioinformatics Advances, 3(1), vbad155 (9 pages), 2023.
6. K. Matsuda, A. Shirakami, R. Nakajima, T. Akutsu, and M. Shimono, Whole-brain evaluation of cortical microconnectomes,
   eNeuro, 10(10), ENEURO.0094-23.2023 (17 pages), 2023.
7. Y. Cao, W. Pi, C-Y. Lin, U. Münzner, M. Ohtomo, and T. Akutsu, Common attractors in multiple Boolean networks,
   IEEE/ACM Transactions on Computational Biology and Bioinformatics, 20(5), 2862-2873, 2023.
8. F. Li, C. Wang, X. Guo, T. Akutsu, G. I. Webb, L. J. M. Coin, L. Kurgan, and J. Song J, ProsperousPlus: a one-stop and comprehensive platform for accurate protease-specific substrate cleavage prediction and machine-learning model construction (Problem Solving Protocol),
   Briefings in Bioinformatics, 24(6), bbad372 (14 pages) , 2023.
9. K. Shiota, A. Suma, H. Ogawa, T. Yamaguchi, A. Iida, T. Hata, M. Matsushita, T. Akutsu, and M. Tateno, AQDnet: Deep neural network for protein-ligand docking simulation,
   ACS Omega. 2023(8), 23925-23935, 2023.
10. J. Xu, F. Li, C. Li, X. Guo, C. Landersdorfer, H-H. Shen, A. Y. Peleg, J. Li, S. Imoto, J. Yao, T. Akutsu, and J. Song, iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities (Problem Solving Protocol).
    Briefings in Bioinformatics, 24(4), bbad240 (20 pages), 2023.
11. X. Jia, P. Zhao, F. Li, Z. Qin, H. Ren, J. Li, C. Miao, Q. Zhao, T. Akutsu, G. Dou, Z. Chen, and J. Song, ResNetKhiib: a novel cell type-specific tool for predicting lysine 2-hydroxyisobutylation sites via transfer learning (Problem Solving Protocol),
    Briefings in Bioinformatics, 24(2), bbad063 (13 pages), 2023.
12. T. Mori, T. Takase, K-C. Lan, J. Yamane, C. Alev, A. Kimura, K. Osafune, J. K. Yamashita, T. Akutsu, H. Kitano, and W. Fujibuchi, eSPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio-temporal architectures of cells,
    BMC Bioinformatics, 24(1), 252 (27 pages), 2023.
13. T. Pan, C. Li, Y. Bi, Z. Wang, R. B. Gasser, A. N. Purcell, T. Akutsu, G. I. Webb, S. Imoto, and J. Song, PFresGO: an attention mechanism-based deep-learning approach for protein annotation by integrating gene ontology inter-relationships,
    Bioinformatics, 39(3), btad094 (8 pages), 2023.
14. A. A. Melkman, S. Guo, W-K. Ching, P. Liu, and T. Akutsu, On the compressive power of Boolean threshold autoencoders,
    IEEE Transactions on Neural Networks and Learning Systems, 34(2), 921-931, 2023.
15. J. Chen, G. Han, A. Xu, T. Akutsu, and H. Cai, Identifying miRNA-gene common and specific regulatory modules for cancer subtyping by a high-order graph matching model,
    IEEE/ACM Transactions on Computational Biology and Bioinformatics, 20(1), 421-431, 2023.
16. F. Zhang, J. Zhu, R. Chiewvanichakorn, A. Shurbevski, H. Nagamochi, and T. Akutsu, A new approach to the design of acyclic chemical compounds using skeleton trees and integer linear programming,
    Applied Intelligence, 52(15), 17058-17072, 2022.
    Preliminary version has appeared in AIE/IEA 2020.
17. C. Liu, J. Song, H. Ogata, and T. Akutsu. MSNet-4mC: learning effective multi-scale representations for identifying DNA N4-methylcytosine sites,
    Bioinformatics, 38(23), 5160-5167, 2022.
18. R. Li, J-Y. Lee, J-M. Yang, and T. Akutsu, Densest subgraph-based methods for protein-protein interaction hot spot prediction,
    BMC Bioinformatics, 23(1), 451 (12 pages), 2022.
19. J. Zhu, N. A. Azam, F. Zhang, A. Shurbevski, K. Haraguchi, L. Zhao, H. Nagamochi, and T. Akutsu, A novel method for inferring chemical compounds with prescribed topological substructures based on integer programming,
    IEEE/ACM Transactions on Computational Biology and Bioinformatics, in press. 19(6), 3233-3245, 2022.
20. Z. Chen, X. Liu, P. Zhao, C. Li, Y. Wang, F. Li, T. Akutsu, C. Bain, R. B. Gasser, J. Li, Z. Yang, X. Gao, L. Kurgan, and J. Song, iFeatureOmega: an integrative platform for engineering, visualization and analysis of features from molecular sequences, structural and ligand data sets,
    Nucleic Acids Research (Web Server Issue), 50(W1), 434-447, 2022.
21. S. Iqbal, F. Ge, F. Li, T. Akutsu, Y. Zheng, R. B. Gasser, D. J. Yu, G. I. Webb, and J. Song, PROST: AlphaFold2-aware sequence-based predictor to estimate protein stability changes upon missense mutations,
    Journal Chemical Information and Modeling, 62(17), 4270-4282, 2022.
22. S. Guo, P. Liu, W-K. Ching, and T. Akutsu, On the distribution of successor states in Boolean threshold networks,
    IEEE Transactions on Neural Networks and Learning Systems, 33(9), 4147-4159, 2022.
23. J. Zhu, N. A. Azam, K. Haraguchi, L. Zhao, H. Nagamochi, and T. Akutsu, An inverse QSAR method based on linear regression and integer programming,
    Frontiers in Bioscience-Landmark, 77(6), 188 (14 pages), 2022.
    Preliminary version has appeared in ICBBB 2022.
24. T. Mori and T. Akutsu, Attractor detection and enumeration algorithms for Boolean networks (Mini Review),
    Computational and Structural Biotechnology Journal, 20. 2512-2520, 2022.
25. M. Zhang, C. Jia, F. Li, C. Li, Y. Zhu, T. Akutsu, G. I. Webb, Q. Zou, L. J. M. Coin, and J. Song, Critical assessment of computational tools for prokaryotic and eukaryotic promoter prediction (Review),
    Briefings Bioinformatics, 23(2), bbab551 (25 pages), 2022.
26. G. V. Trinh, T. Akutsu, and K. Hiraishi, An FVS-based approach to attractor detection in asynchronous random Boolean networks,
    IEEE/ACM Transactions on Computational Biology and Bioinformatics, 19(2), 806-818, 2022.
27. S. Kumano and T. Akutsu, Comparison of the representational power of random forests, binary decision diagrams, and neural networks,
    Neural Computation, 34(4), 1019-1044, 2022.
28. U. Münzner, T. Mori, M. Krantz, E. Klipp, and T. Akutsu, Identification of periodic attractors in Boolean networks using a priori information,
    PLoS Computational Biology, 18(1), e1009702 (27 pages), 2022.
29. F. Wang, Y-T. Chen, J-M. Yang, and T. Akutsu, A novel graph convolutional neural network for predicting interaction sites on protein kinase inhibitors in phosphorylation,
    Scientific Reports, 12(1), 229 (11 pages), 2022.
30. Y. Wang, F. Li, M. Bharathwaj, N. C. Rosas, A. Leier, T. Akutsu, G. I. Webb, T. T. Marquez-Lago, J. Li, T. Lithgow, and J. Song, DeepBL: a deep learning-based approach for in silico discovery of beta-lactamases (Problem Solving Protocol),
    Briefings in Bioinformatics, 22(4), bbaa301 (12 pages), 2021.
31. Y. Zhu, F. Li, D. Xiang, T. Akutsu, J. Song, and C. Jia, Computational identification of eukaryotic promoters based on cascaded deep capsule neural networks (Problem Solving Protocol),
    Briefings in Bioinformatics, 22(4), bbaa299 (11pages), 2021.
32. H. Nagamochi, J. Zhu, N. A. Azam, K. Haraguchi, L. Zhao, and T. Akutsu, Integer linear programming-based methods for inverse QSAR (in Japanese),
    Journal of Computer Chemistry, Japan, 20(3), 106-111, 2021.
33. E. Yamaguchi, T. Akutsu, and J. C. Nacher, Probabilistic critical controllability analysis of protein interaction networks integrating normal brain ageing gene expression profiles,
    International Journal of Molecular Sciences, 22(18), 9891, 2021.
34. N. Nakajima, T. Hayashi, K. Fujiki, K. Shirahige, T. Akiyama, T. Akutsu, and R. Nakato, Codependency and mutual exclusivity for gene community detection from sparse single-cell transcriptome data,
    Nucleic Acids Research, 49(18), e104 (13 pages), 2021.
35. S. Iqbal, F. Li, T. Akutsu, D. B. Ascher, G. I. Webb, and J. Song, Assessing the performance of computational predictors for estimating protein stability changes upon missense mutations (Review Article),
    Briefings in Bioinformatics, 22(6), bbab184 (23 pages), 2021.
36. S. Mei, F. Li, D. Xiang, R. Ayala, P. Faridi, G. I. Webb, P. T. Illing, J. Rossjohn, T. Akutsu, N. P. Croft, A. N. Purcell, and J. Song, Anthem: a user customised tool for fast and accurate prediction of binding between peptides and HLA class I molecules (Problem Solving Protocol),
    Briefings in Bioinformatics, 22(5), bbaa415 (16 pages), 2021.
37. N. A. Azam, J. Zhu, Y. Sun, Y. Shi, A. Shurbevski, L. Zhao, H. Nagamochi, and T. Akutsu, A novel method for inference of acyclic chemical compounds with bounded branch-height based on artificial neural networks and integer programming,
    Algorithms for Molecular Biology, 16(1), 18 (39 pages), 2021.
38. T. Akutsu, J. Jansson, R. Li, A. Takasu, and T. Tamura, New and improved algorithms for unordered tree inclusion,
    Theoretical Computer Science, 883, 83-98, 2021.
    Preliminary version has appeared in ISAAC 2018.
39. R. Xie, J. Li, J. Wang, W. Dai, A. Leier, T. T. Marquez-Lago, T. Akutsu, T. Lithgow, J. Song, and Y. Zhang, DeepVF: a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy (Problem Solving Protocol),
    Briefings in Bioinformatics, 22(3), bbaa125 (15 pages), 2021.
40. X. Cheng, W-K. Ching, S. Guo, and T. Akutsu, Discrimination of attractors with noisy nodes in Boolean networks (Technical Communique),
    Automatica, 130, 109630, 2021.
41. Z. Chen, P. Zhao, C. Li, F. Li, D. Xiang, Y. Z. Chen, T. Akutsu, R. J. Daly RJ, G. I. Webb, Q. Zhao, L. Kurgan, and J. Song, iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization,
    Nucleic Acids Research, 49(10), e60, 2021.
42. T. Shinzawa, T. Akutsu, and J. C. Nacher, Uncovering and classifying the role of driven nodes in control of complex networks,
    Scientific Reports, 11(1), 9627, 2021.
43. M. Kajiwara, R. Nomura, F. Goetze, M. Kawabata, Y. Isomura, T. Akutsu, and M. Shimono, Inhibitory neurons exhibit high controlling ability in the cortical microconnectome,
    PLoS Computational Biology, 17(4), e1008846, 2021.
44. R. Li, C-Y. Lin, W-F. Guo, and T. Akutsu, Weighted minimum feedback vertex sets and implementation in human cancer genes detection,
    BMC Bioinformatics, 22(1), 143, 2021.
45. Y. Shi, J. Zhu, N. A. Azam, K. Haraguchi, L. Zhao, H. Nagamochi, and T. Akutsu, An inverse QSAR method based on a two-layered model and integer programming,
    International Journal of Molecular Sciences, 22, 2847, 2021.
46. P. Liu, J. Song, C-Y. Lin, and T. Akutsu, ReCGBM: a gradient boosting-based method for predicting human Dicer cleavage sites,
    BMC Bioinformatics, 22(1), 63, 2021.
47. F. Wang, T. Akutsu, and T. Mori, Comparison of pseudoknotted RNA secondary structures by topological centroid identification and tree edit distance,
    Journal of Computational Biology, 27(9), 1443-1451, 2020.
48. Z. Chen, P. Zhao, F. Li, Y. Wang, A. I. Smith, G. I. Webb, T. Akutsu, A. Baggag, H. Bensmail, and J. Song, Comprehensive review and assessment of computational methods for predicting RNA post-transcriptional modification sites from RNA sequences (Review Article),
    Briefings in Bioinformatics, 21(5), 1676-1696, 2020.
49. F. Li, A. Leier, Q. Liu, Y. Wang, D. Xiang, T. Akutsu, G. I. Webb, A. I. Smith, T. Marquez-Lago, J. Li, and J. Song, Procleave: predicting protease-specific substrate cleavage sites by combining sequence and structural information,
    Genomics, Proteomics & Bioinformatics, 18(1), 52-64, 2020.
50. W-F, Guo, S-W. Zhang, T. Zeng, T. Akutsu, and L. Chen, Network control principles for identifying personalized driver genes in cancer (Review Article),
    Briefings in Bioinformatics, 21(5), 1641-1662, 2020.
51. S. Mei, F. Li, A. Leier, T. T. Marquez-Lago, K. Giam, N. P. Croft, T. Akutsu, A. I. Smith, J. Li, J. Rossjohn, A. W. Purcell, and J. Song, A comprehensive review and performance evaluation of bioinformatic tools for HLA class I peptide binding prediction (Review Article),
    Briefings in Bioinformatics, 21(4), 1119-1135, 2020.
52. Z. Chen, P. Zhao, F. Li, T. T. Marquez-Lago, A. Leier, J. Revote, Y. Zhu, D. R. Powell, T. Akutsu, G. I. Webb, K. C. Chou, A. T. Smith, R. J. Daly, J. Li, and J. Song, iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data (Problem Solving Protocol), Briefings in Bioinformatics, 21(3), 1047-1057, 2020.
53. J. Zhu, C. Wang, A. Shurbevski, H. Nagamochi, and T. Akutsu, A novel method for inference of chemical compounds of cycle index two with desired properties based on artificial neural networks and integer programming,
    Algorithms, 13, 124 (30 pages), 2020.
54. P. Liu, A. A. Melkman, and T. Akutsu, Extracting Boolean and probabilistic rules from trained neural networks,
    Neural Networks, 126, 300-311, 2020.
55. T. Akutsu, A. A. Melkman, and T. Tamura, Improved hardness of maximum common subgraph problems on labeled graphs of bounded treewidth and bounded degree,
    International Journal of Foundations of Computer Science, 31(2), 253-273, 2020.
56. F. Li, J. Chen, A. Leier, T. Marquez-Lago, Q. Liu, Y. Wang, J. Revote, A. I. Smith, T. Akutsu, G. I. Webb, L. Kurgan, and J. Song, DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites,
    Bioinformatics, 36(4), 1057-1065, 2020.
57. S. Nakashima, J. C. Nacher, J. Song, and T. Akutsu, An overview of bioinformatics methods for analyzing autism spectrum disorders (Review Article),
    Current Pharmaceutical Design, 25(43), 4552-4559, 2020.
58. Y. Zhang, S. Yu, R. Xie, J. Li, A. Leier, T. T. Marquez-Lago, T. Akutsu, A. I. Smith, Z. Ge, J. Wang, T. Lithgow, and J. Song, PeNGaRoo, a combined gradient boosting and ensemble learning framework for predicting non-classical secreted proteins,
    Bioinformatics. 36(3), 704-712, 2020.
59. J. Zeng, H. Cai, H. Peng, H. Wang, Y. Zhang, and T. Akutsu, Causalcall: nanopore basecalling using a temporal convolutional network,
    Frontiers in Genetics, 10, 1332 (11 pages), 2020.
60. Y. Zhang, R. Xie, J. Wang, A. Leier, T. T. Marquez-Lago, T. Akutsu, G. I. Webb, K. C. Chou, and J. Song, Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework (Opinion Article),
    Briefings in Bioinformatics, 20(6), 2185-2199, 2019.
61. Z. Chen, X. Liu, F. Li, C. Li, T. Marquez-Lago, A. Leier, T. Akutsu, G. I. Webb, D. Xu, A. I. Smith, L. Li, K. C. Chou, and J. Song, Large-scale comparative assessment of computational predictors for lysine post-translational modification sites (Review Article),
    Briefings in Bioinformatics, 20(6), 2267-2290, 2019.
62. F. Li, Y, Wang, C. Li, T. T. Marquez-Lago, A. Leier, N. D. Rawlings, G. Haffari, J. Revote, T. Akutsu, K. C. Chou, A. W. Purcell, R. N. Pike, G. I. Webb, A. Ian Smith, T. Lithgow, R. J. Daly, J. C. Whisstock, and J. Song, Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods (Review Article),
    Briefings in Bioinformatics, 20(6), 2150-2166, 2019.
63. Y. Bao, S. Marini, T. Tamura, M. Kamada, S. Maegawa, H. Hosokawa, J. Song, and T. Akutsu, Toward more accurate prediction of caspase cleavage sites: a comprehensive review of current methods, tools and features (Review Article),
    Briefings in Bioinformatics, 20(5), 1669-1684, 2019.
64. S. Itami-Matsumoto, M. Hayakawa, S. Uchida-Kobayashi, M. Enomoto, A. Tamori, K. Mizuno, H. Toyoda, T. Tamura, T. Akutsu, T. Ochiya, N. Kawada, and Y. Murakami, Circulating exosomal miRNA profiles predict the occurrence and recurrence of hepatocellular carcinoma in patients with direct-acting antiviral-induced sustained viral response,
    Biomedicines. 7(4), 87, 2019.
65. H. Koyano, M. Hayashida, and T. Akutsu, Optimal string clustering based on a Laplace-like mixture and EM algorithm on a set of strings,
    Journal of Computer and System Sciences, 106 94-128, 2019.
66. T. Akutsu and A. A. Melkman, Identification of the structure of a probabilistic Boolean network from samples including frequencies of outcomes,
    IEEE Transactions on Neural Networks and Learning Systems, 30(8), 2383-2396, 2019.
67. C-Y. Lin, P. Ruan, R. Li, J-M. Yang, S. See, J. Song, and T. Akutsu, Deep learning with evolutionary and genomic profiles for identifying cancer subtypes,
    Journal of Bioinformatics and Computational Biology, 17(3) (Special Section on CBIM2018), 1940005 (15 pages), 2019.
68. T. Matsubara, T. Ochiai, M. Hayashida, T. Akutsu, and J. C. Nacher, Convolutional neural network approach to lung cancer classification integrating protein interaction network and gene expression profiles,
    Journal of Bioinformatics and Computational Biology, 17(3) (Special Section on CBIM2018), 1940007 (11 pages), 2019.
69. J. Wang, B. Yang, Y. An, T. Marquez-Lago, A. Leier, J. Wilksch, Q. Hong, Y. Zhang, M. Hayashida, T. Akutsu, G. I. Webb, R, A. Strugnell, J. Song, and T. Lithgow, Systematic analysis and prediction of type IV secreted effector proteins by machine learning approaches,
    Briefings in Bioinformatics, 20(3), 931-951, 2019.
70. J-M. Schwartz, H. Otokuni, T. Akutsu, and J. C. Nacher, Probabilistic controllability approach to metabolic fluxes in normal and cancer tissues,
    Nature Communications, 10, 2725 (9 pages), 2019.
71. J. Wang, J. Li, B. Yang, R. Xie, T. T. Marquez-Lago, A. Leier, M. Hayashida, T. Akutsu, Y. Zhang, K. C. Chou, J. Selkrig, T. Zhou, J. Song, and T. Lithgow, Bastion3: a two-layer ensemble predictor of type III secreted effectors,
    Bioinformatics, 35, 2017-2028, 2019.
72. J. Song, Y. Wang, F. Li, T. Akutsu, N. D. Rawlings, G. I. Webb, and K. C. Chou, iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites (Review Article),
    Briefings in Bioinformatics, 20, 638-658, 2019.
73. S. Marini, F. Vitali, S. Rampazzi, A. Demartini, and T. Akutsu, Protease target prediction via matrix factorization,
    Bioinformatics, 35(6), 923-929, 2019.
74. V, Ravindran, J. C. Nacher, T. Akutsu, M. Ishitsuka, A. Osadcenco, V. Sunitha, G. Bagler, J-M. Schwartz, and D. L. Robertson, Network controllability analysis of intracellular signalling reveals viruses are actively controlling molecular systems,
    Scientific Reports, 9, 2066, 2019.
75. Y. Nishiyama, A. Shurbevski, H. Nagamochi, and T. Akutsu, Resource cut, a new bounding procedure to algorithms for enumerating tree-like chemical graphs,
    IEEE/ACM Transactions on Computational Biology and Bioinformatics, 16(1), 77-90, 2019.
76. J. C. Nacher, M. Ishitsuka, S. Miyazaki, and T. Akutsu, Finding and analysing the minimum set of driver nodes required to control multilayer networks,
    Scientific Reports, 9(1), 576 (12 pages), 2019.
77. W. Hou, P. Ruan, W-K. Ching, and T. Akutsu, On the number of driver nodes for controlling a Boolean network when the targets are restricted to attractors,
    Journal of Theoretical Biology, 463, 1-11, 2019.
78. F. Li, C. Li, T. T. Marquez-Lago, A. Leier, T. Akutsu, A. W. Purcell, A. I. Smith, T. Lithgow, R. Daly, J. Song, and K. C. Chou, Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome,
    Bioinformatics, 4, 4223-4231, 2018.
79. T, Tamura, W. Lu, J. Song, and T. Akutsu, Computing minimum reaction modifications in a Boolean metabolic network,
    IEEE/ACM Transactions on Computational Biology and Bioinformatics, 15(2) (Special Section for GIW/InCoB 2015), 1853-1862, 2018.
80. Y. Bao, M. Hayashida, P. Liu, M. Ishitsuka, J. C. Nacher, and T. Akutsu, Analysis of critical and redundant vertices in controlling directed complex networks using feedback vertex sets,
    Journal of Computational Biology, 25, 1071-1090, 2018.
81. J. Wang, B. Yang, A. Leier, T. T. Marquez-Lago, M. Hayashida, A. Rocker, Z. Yanju, T. Akutsu, K. C. Chou, R. A. Strugnell, J. Song, and T. Lithgow, Bastion6: a bioinformatics approach for accurate prediction of type VI secreted effectors,
    Bioinformatics, 34(15), 2546-2555, 2018.
82. T. Mori, H. Ngouv, M. Hayashida, T. Akutsu, and J. C. Nacher, ncRNA-disease association prediction based on sequence information and tripartite network,
    BMC Systems Biology 12, Suppl 1 (Supplement for APBC 2018), 37 (11 pages), 2018.
83. N. Nakajima, M. Hayashida, J. Jansson, O. Maruyama, and T. Akutsu, Determining the minimum number of protein-protein interactions required to support known protein complexes,
    PLoS One, 13(4), e0195545 (17 pages), 2018.
84. J. Li, H. Nagamochi, and T. Akutsu, Enumerating substituted benzene isomers of tree-like chemical graphs,
    IEEE/ACM Transactions on Computational Biology and Bioinformatics, 15(2), 633-646, 2018.
85. A. A. Melkman, X. Cheng, W-K. Ching, and T. Akutsu, Identifying a probabilistic Boolean threshold network from samples,
    IEEE Transactions on Neural Networks and Learning Systems, 29(4), 869-881, 2018.
86. P. Ruan, M. Hayashida, T. Akutsu, and J-P. Vert, Improving prediction of heterodimeric protein complexes using combination with pairwise kernel,
    BMC Bioinformatics, 19, Suppl 1 (Supplement for GIW 2017), 73-84, 2018.
87. J. Song, F. Li, A. Leier, T. T. Marquez-Lago. T. Akutsu, G. Haffari, K-C. Chou, G. I. Webb, and R. N. Pike, PROSPERous: high-throughput prediction of substrate cleavage sites for 90 proteases with improved accuracy (Applications Note),
    Bioinformatics, 34(4), 684-687, 2018.
88. L. Liu, T. Mori, Y. Zhao, M. Hayashida, and T. Akutsu, Euler string-based compression of tree-structured data and its application to analysis of RNAs,
    Current Bioinformatics, 13(1), 25-33, 2018.
89. J. Song, F. Li, K. Takemoto, G. HaHaffari, T. Akutsu, K-C. Chou, and G. I. Webb. PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework.
    Journal of Theoretical Biology, 443, 125-137, 2018.
90. M. Ishitsuka, T. Akutsu and J. C. Nacher, Critical controllability analysis of directed biological networks using efficient graph reduction,
    Scientific Reports, 7, 14361 (10 pages), 2017.
91. J. Song, H. Wang, J. Wang, A, Leier, T. Marquez-Lago, B. Yang, Z. Zhang, T. Akutsu, G. I. Webb and R. J. Daly, PhosphoPredict: A bioinformatics tool for prediction of human kinase-specific phosphorylation substrates and sites by integrating heterogeneous feature selection,
    Scientific Reports, 7, 6862 (19 pages), 2017.
92. F. Li, J. Song, C. Li, T. Akutsu and Y. Zhang, PAnDE: Averaged n-dependence estimators for positive unlabeled learning,
    ICIC Express Letters, Part B: Applications, 8, 1287-1297, 2017.
93. X. Cheng, T. Tamura, W-K. Ching and T. Akutsu. Discrimination of singleton and periodic attractors in Boolean networks,
    Automatica, 84, 205-213, 2017.
94. T. Akutsu, J. Jansson, A. Takasu and T. Tamura, On the parameterized complexity of associative and commutative unification,
    Theoretical Computer Science, 660, 57-74, 2017.
    Preliminary version has appeared in IPEC 2014.
95. Y. An, J. Wang, J. Song, C. Li, J. Revote, T. Naderer, M. Hayashida, T. Akutsu, Y. Zhang, G. I. Webb and T. Lithgow, SecretEPDB: a comprehensive web-based resource for secreted effector proteins of the bacterial types III, IV and VI secretion systems,
    Scientific Reports, 7, 41031 (10 pages), 2017.
96. Y. Kato, T. Mori, K. Sato, S. Maegawa, H. Hosokawa and T. Akutsu, An accessibility-incorporated method for accurate prediction of RNA-RNA interactions from sequence data,
    Bioinformatics, 33, 202-209, 2017.
97. X. Cheng, T. Mori, Y. Qiu, W-K. Ching and T. Akutsu, Exact identification of the structure of a probabilistic Boolean network from samples,
    IEEE/ACM Transactions on Computational Biology and Bioinformatics, 13, 1107-1116, 2016.
98. Y. Bao, M. Hayashida and T. Akutsu, LBSizeCleav: improved support vector machine (SVM)-based prediction of Dicer cleavage sites using loop/bulge length,
    BMC Bioinformatics, 17, 487 (11 pages), 2016.
99. W. Hou, T. Tamura, W-K. Ching and T. Akutsu, Finding and analyzing the minimum set of driver nodes in control of Boolean networks,
    Advances in Complex Systems, 19, 1650006 (32 pages), 2016.
100. J. Jindalertudomdee, M. Hayashida and T. Akutsu, Enumeration method for structural isomers containing user-defined structures based on breadth-first search approach,
     Journal of Computational Biology, 23, 625-640, 2016.
101. K. Takemoto and T. Akutsu, Analysis of the effect of degree correlation on the size of minimum dominating sets in complex networks,
     PLoS ONE, 11(6), e0157868 (11 pages), 2016.
102. M. Hayashida and T. Akutsu, Complex network-based approaches to biomarker discovery (Review Paper),
     Biomarkers in Medicine, 10(6), 621-632, 2016.
103. J. C. Nacher and T. Akutsu, Minimum dominating set-based methods for analyzing biological networks (Invited Review Paper),
     Methods, 102, 57-63, 2016.
104. M. Ishitsuka, T. Akutsu and J. C. Nacher, Critical controllability in proteome-wide protein interaction network integrating transcriptome,
     Scientific Reports, 6, 23541 (13 pages), 2016.
105. H. Koyano, M. Hayashida and T. Akutsu, Maximum margin classifier working in a set of strings,
     Proceedings of the Royal Society A, 472, 20150551 (17 pages), 2016.
106. C. Li, C. C. H. Chang, B. T. Porebski, M. Hayashida, T. Akutsu, J. Song and A. M. Buckle, Critical evaluation of in silico methods for prediction of coiled-coil domains in proteins (Review Paper),
     Briefings in Bioinformatics, 17. 270-282, 2016.
107. J. Jindalertudomdee, M. Hayashida, Y. Zhao and T. Akutsu, Enumeration method for tree-like chemical compounds with benzene rings and naphthalene rings by breadth-first search order,
     BMC Bioinformatics, 17, 113 (16 pages), 2016.
108. T. Hasegawa, A. Niida, T. Mori, T. Shimamura, R. Yamaguchi, S. Miyano, T. Akutsu and S. Imoto, A likelihood-free filtering method via approximate Bayesian computation in evaluating biological simulation models,
     Computational Statistics and Data Analysis, 94, 63-74, 2016.
109. H. Kagami, T. Akutsu, S. Maegawa, H. Hosokawa and J. C. Nacher, Determining associations between human diseases and non-coding RNAs with critical roles in network control,
     Scientific Reports, 5, 14577 (11 pages), 2015.
110. X. Cong and T. Akutsu, Matrix Network: a new data structure for efficient enumeration of microstates of a genetic regulatory network,
     Journal of Information Processing, 23, 804-813, 2015.
111. T. Mori, A. Takasu, J. Jansson, J. Hwang, T. Tamura and T. Akutsu, Similar subtree search using extended tree inclusion,
     IEEE Transactions on Knowledge and Data Engineering, 27, 3360-3373, 2015.
112. T. Mori, M. Floettmann, M. Krantz, T. Akutsu and E. Klipp, Stochastic simulation of Boolean rxncon models: towards quantitative analysis of large signaling networks,
     BMC Systems Biology, 9, 45 (9 pages), 2015.
113. T. Akutsu, T. Tamura, A. A. Melkman and A. Takasu, On the complexity of finding a largest common subtree of bounded degree,
     Theoretical Computer Science, 590. 2-16, 2015.
     Preliminary version has appeared in FCT 2013.
114. T. Tamura, W. Lu and T. Akutsu, Computational methods for modification of metabolic networks (Invited Mini-Review Paper),
     Computational and Structural Biotechnology Journal, 13. 376-381, 2015.
115. M. Hayashida, J. Jindalertudomdee, Y. Zhao and T. Akutsu, Parallelization of enumerating tree-like chemical compounds by breadth-first search order,
     BMC Medical Genomics, 8, Suppl 2 (Suppl for TBC/ISB 2014), S15 (7 pages), 2015.
116. Y. Zhao, M. Hayashida, Y. Cao, J. Hwang and T. Akutsu, Grammar-based compression approach to extraction of common rules among multiple trees of glycans and RNAs,
     BMC Bioinformatics, 16, 128 (13 pages), 2015.
117. T. Hasegawa, T. Mori, R. Yamaguchi, T. Shimamura, S. Miyano, S. Imoto and T. Akutsu, Genomic data assimilation using a higher moment filtering technique for restoration of gene regulatory networks,
     BMC Systems Biology, 9, 14 (13 pages), 2015.
118. W. Lu, T. Tamura, J. Song and T. Akutsu, Computing smallest intervention strategies for multiple metabolic networks in a Boolean model,
     Journal of Computational Biology, 22, 85-110, 2015.
119. L. Uechi, T. Akutsu, H. E. Stanley, A. J. Marcus and D. Y. Kenett, Sector dominance ratio analysis of financial markets,
     Physica A, 421, 488-509, 2015.
120. J. C. Nacher and T. Akutsu, Structurally robust control of complex networks,
     Physical Review E, 91, 012826, 2015.
121. C-J. Chang, T. Tamura, K-M. Chao and T. Akutsu, A fixed-parameter algorithm for detecting a singleton attractor in an AND/OR Boolean network with bounded treewidth,
     IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E98-A, 384-390, 2015.
122. J. C. Nacher and T. Akutsu, Analysis of critical and redundant nodes in controlling directed and undirected complex networks using dominating sets,
     Journal of Complex Networks, 2, 394-412, 2014.
     Preliminary version has appeared in SITIS 2013.
123. T. Hasegawa, T. Mori, R. Yamaguchi, S. Imoto, S. Miyano and T. Akutsu, An efficient data assimilation schema for restoration and extension of gene regulatory networks using time-course observation data,
     Journal of Computational Biology, 21, 785-798, 2014.
124. H. Koyano, T. Tsubouchi, H. Kishino and T. Akutsu, Archaeal beta diversity patterns under the seafloor along geochemical gradients,
     Journal of Geophysical Research - Biogeosciences, 119, 1770-1788, 2014.
125. M. Kamada, Y. Sakuma, M. Hayashida and T. Akutsu, Prediction of protein-protein interaction strength using domain features with supervised regression,
     The Scientific World Journal, 2014, 240673 (7 pages), 2014.
     Preliminary version has appeared in PDPTA 2013.
126. M. Hayashida, P. Ruan and T. Akutsu, Proteome compression via protein domain compositions,
     Methods, 67, 380-385, 2014.
127. M. Suzuki, H. Nagamochi and T. Akutsu, Efficient enumeration of monocyclic chemical graphs with given path frequencies,
     Journal of Cheminformatics, 6, 31 (18 pages), 2014.
128. M. Hayashida and T. Akutsu, Domain-based approaches to prediction and analysis of protein-protein interactions (Invited Review Paper),
     International Journal of Knowledge Discovery in Bioinformatics, 4, 24-41, 2014.
129. T. Akutsu, T. Tamura, D. Fukagawa and A. Takasu, Efficient exponential-time algorithms for edit distance between unordered trees,
     Journal of Discrete Algorithms, 25, 79-93, 2014.
     Preliminary version has appeared in CPM 2012.
130. N. Nakajima and T. Akutsu, Network completion for static gene expression data,
     Advances in Bioinformatics, 2014, 382452 (9 pages), 2014.
131. H. Cai, P. Ruan, M. Ng and T. Akutsu, Feature weight estimation for gene selection: a local hyperlinear learning approach,
     BMC Bioinformatics, 15, 70 (13 pages), 2014.
132. W. Lu, T. Tamura, J. Song and T. Akutsu, Integer programming-based method for designing synthetic metabolic networks by minimum reaction insertion in a Boolean model,
     PLoS ONE, 9, e92637 (14 pages), 2014.
133. N. Nakajima and T. Akutsu, Exact and heuristic methods for network completion for time varying genetic networks,
     BioMed Research International, 2014, 684014 (13 pages), 2014.
     Preliminary version has appeared in IIBM 2013.
134. P. Ruan, M. Hayashida, O. Maruyama and T. Akutsu, Prediction of heterotrimeric protein complexes by two-phase learning using neighboring kernels,
     BMC Bioinformatics, 15, Suppl 2 (Suppl. for APBC 2014), S6 (6 pages), 2014.
135. Y. Qiu, T. Tamura, W-K. Ching and T. Akutsu, On control of singleton attractors in multiple Boolean networks: integer programming-based method,
     BMC Systems Biology, 8, Suppl 1 (Suppl. for APBC 2014), S7 (10 pages), 2014.
136. M. Wang, X-M. Zhao, H. Tan, T. Akutsu, J. C. Whisstock and J. Song, Cascleave 2.0, a new approach for predicting caspase and granzyme cleavage targets,
     Bioinformatics, 30, 71-80, 2014.
137. Y. Zhao, M. Hayashida, J. Jindalertudomdee, H. Nagamochi and T. Akutsu, Breadth first search approach to enumeration of tree-like chemical compounds,
     Journal of Bioinformatics and Computational Biology, 11 (Special Issue for GIW 2013), 1343007 (19 pages), 2013.
138. M. Hayashida, M. Kamada, J. Song and T. Akutsu, Prediction of protein-RNA residue-base contacts using two-dimensional conditional random field with the lasso,
     BMC Systems Biology, 7, Suppl 2 (Suppl. for ISB 2012), S15 (11 pages), 2013.
     Preliminary version has appeared in ISB 2012.
139. A. A. Melkman and T. Akutsu, An improved satisfiability algorithm for nested canalyzing functions and its application to determining a singleton attractor of a Boolean network,
     Journal of Computational Biology, 20, 958-969, 2013.
140. H. Jiang, T. Tamura, W-K. Ching and T. Akutsu, On the complexity of inference and completion of Boolean networks from given singleton attractors,
     IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E96-A, 2265-2274, 2013.
141. L. Uechi and T. Akutsu, Stability and restoration phenomena in competitive systems,
     Progress of Theoretical and Experimental Physics, 2013, 103J01 (18 pages), 2013.
142. K. Takemoto, T. Tamura and T. Akutsu, Theoretical estimation of metabolic network robustness against multiple reaction knockouts using branching process approximation,
     Physica A, 392, 5525-5535, 2013.
143. Y. Zhao, T. Tamura, T. Akutsu and J-P. Vert, Flux balance impact degree: A new definition of impact degree to properly treat reversible reactions in metabolic networks,
     Bioinformatics, 29, 2178-2185, 2013.
144. M. Wang, Z. Sun, T. Akutsu and J. Song, Recent advances in predicting functional impact of single amino acid polymorphisms: a review of useful features, computational methods and available tools (Review Paper),
     Current Bioinformatics, 8, 161-176, 2013.
145. P. Ruan, M. Hayashida, O. Maruyama and T. Akutsu, Prediction of heterodimeric protein complexes from weighted protein-protein interaction networks using novel features and kernel functions,
     PLoS ONE, 8, e65265 (7 pages), 2013.
146. Y. Lai, M. Hayashida and T. Akutsu, Survival analysis by penalized regression and matrix factorization,
     The Scientific World Journal, 2013, 632030 (11 pages), 2013.
147. J. C. Nacher and T. Akutsu, Structural controllability of unidirectional bipartite networks,
     Scientific Reports, 3, 1647 (8 pages), 2013.
148. X. Chen, T. Akutsu, T. Tamura and W-K. Ching, Finding optimal control policy in probabilistic Boolean networks with hard constraints by using integer programming and dynamic programming,
     International Journal of Data Mining and Bioinformatics, 7, 322-343, 2013.
     Preliminary version has appeared in BIBM 2010.
149. T. Akutsu and H. Nagamochi, Comparison and enumeration of chemical graphs (Invited Review Paper),
     Computational and Structural Biotechnology Journal, 5, e201302004 (9 pages), 2013.
150. T. Akutsu and T. Tamura, A polynomial-time algorithm for computing the maximum common connected edge subgraph of outerplanar graphs of bounded degree,
     Algorithms, 6, 119-135, 2013.
     Preliminary version has appeared in MFCS 2012.
151. T. Akutsu, D. Fukagawa, M. M. Halldorsson, A. Takasu and K. Tanaka, Approximation and parameterized algorithms for common subtrees and edit distance between unordered trees,
     Theoretical Computer Science, 470, 10-22, 2013.
     Preliminary results appeared in SPIRE 2009 (by DF, TA, AT) and ISAAC 1996 (by MMH, KT).
152. J. Song, H. Tan, A. J. Perry, T. Akutsu, G. I. Webb, J. C. Whisstock and R. N. Pike, PROSPER: an integrated feature-based tool for predicting protease substrate cleavage sites,
     PLoS ONE, 7, e50300 (23 pages), 2012.
153. K. Sato, Y. Kato, T. Akutsu, K. Asai and Y. Sakakibara, DAFS: simultaneous aligning and folding of RNA sequences via dual decomposition,
     Bioinformatics, 28, 3218-3224, 2012.
154. T. Akutsu, Y. Zhao, M. Hayashida and T. Tamura, Integer programming-based approach to attractor detection and control of Boolean networks,
     IEICE Transactions on Information and Systems, E95-D, 2960-2970, 2012.
     Preliminary version has appeared in CDC/CCC 2009.
155. C. Zheng, M. Wang, K. Takemoto, T. Akutsu, Z. Zhang and J. Song, An integrative computational framework based on a two-step random forest algorithm improves prediction of zinc-binding sites in proteins,
     PLoS ONE, 7, e49716 (15 pages), 2012.
156. N. Nakajima, T. Tamura, Y. Yamanishi, K. Horimoto and T. Akutsu, Network completion using dynamic programming and least-squares fitting,
     The Scientific World Journal, 2012, 957620 (8 pages), 2012.
157. T. Mori, T. Tamura, D. Fukagawa, A. Takasu, E. Tomita and T. Akutsu, A clique-based method using dynamic programming for computing edit distance between unordered trees,
     Journal of Computational Biology, 19, 1089-1104, 2012.
158. Y. Zhao, M. Hayashida, J. C. Nacher, H. Nagamochi and T. Akutsu, Protein complex prediction via improved verification methods using constrained domain-domain matching,
     International Journal of Bioinformatics Research and Applications, 8, 210-227, 2012.
159. T. Akutsu, S. Kosub, A. A. Melkman and T. Tamura, Finding a periodic attractor of a Boolean network,
     IEEE/ACM Transactions on Computational Biology and Bioinformatics, 9, 1410-1421, 2012.
160. M. Wang, X-M. Zhao, K. Takemoto, H. Xu, Y. Li, T. Akutsu and J. Song, FunSAV: predicting the functional effect of single amino acid variants using a two-stage random forest model,
     PLoS ONE, 7, e43847 (14 pages), 2012.
161. J. C. Nacher and T. Akutsu, Dominating scale-free networks with variable scaling exponent: Heterogeneous networks are not difficult to control,
     New Journal of Physics, 14, 073005, 2012.
162. Y. Kato, K. Sato, K. Asai and T. Akutsu, Rtips: fast and accurate tools for RNA 2D structure prediction using integer programming,
     Nucleic Acids Research, 40, Web Server Issue, W29-W34, 2012.
163. L. Uechi and T. Akutsu, Conservation laws and symmetries in competitive systems,
     Progress of Theoretical Physics Supplement, No. 194, 210-222, 2012.
     (Proc. the YITP Workshop on Econophysics)
164. T. Akutsu, D. Fukagawa, J. Jansson and K. Sadakane, Inferring a graph from path frequency,
     Discrete Applied Mathematics, 160, 1416-1428, 2012.
     Preliminary version has appeared in CPM 2005.
165. M. Hayashida, P. Ruan and T. Akutsu, A quadsection algorithm for grammar-based image compression,
     Integrated Computer-Aided Engineering, 19, 23-38, 2012.
     Preliminary version has appeared in Proc. FGIT 2010.
166. J. Song, H. Tan, M. Wang, G. I. Webb and T. Akutsu, Two-level support vector regression approach for protein backbone torsion angle prediction from primary sequences,
     PLoS ONE, 7, e30361 (16 pages), 2012.
167. T. Akutsu, A. A. Melkman and T. Tamura, Singleton and 2-periodic attractors of sign-definite Boolean networks,
     Information Processing Letters, 112, 35-38, 2012.
168. K. Takemoto, T. Tamura, Y. Cong, W-K. Ching, J-P. Vert and T. Akutsu, Analysis of the impact degree distribution in metabolic networks using branching process approximation,
     Physica A, 391, 379-397, 2012.
169. M. Shimizu, H. Nagamochi and T. Akutsu, Enumerating tree-like chemical graphs with given upper and lower bounds on path frequencies,
     BMC Bioinformatics, 12, Suppl 14 (Suppl. for GIW 2011), S3 (9 pages), 2011.
170. T. Tamura, Y. Cong, T. Akutsu and W-K. Ching, An efficient method of computing impact degrees for multiple reactions in metabolic networks with cycles,
     IEICE Transactions on Information and Systems, E94-D, 2393-2399, 2011.
     Preliminary version has appeared in DTMBIO 2009.
171. M. Hayashida and T. Akutsu, Measuring the similarity of protein structures using image compression algorithms,
     IEICE Transactions on Information and Systems, E94-D, 2468-2478, 2011.
     Preliminary version has appeared in APBC 2008.
172. U. Poolsap, Y. Kato, K. Sato and T. Akutsu, Using binding profiles to predict RNA binding sites of target RNAs,
     Journal of Bioinformatics and Computational Biology, 9, 697-713, 2011.
     Preliminary version has appeared in PSB 2010.
173. T. Imada, S. Ota, H. Nagamochi and T. Akutsu, Efficient enumeration of stereoisomers of outerplanar chemical graphs using dynamic programming,
     Journal of Chemical Information and Modeling, 51, 2788-2807, 2011.
174. O. Demir-Kavuk, M. Kamada, T. Akutsu and E-W. Knapp, Prediction using step-wise L1, L2 regularization and feature selection for small data sets with large number of features,
     BMC Bioinformatics, 12, 412 (10 pages), 2011.
175. T. Akutsu and H. Nagamochi, Kernel methods for chemical compounds: From classification to design (Invited Survey Paper),
     IEICE Transactions on Information and Systems, E94-D, 1846-1853, 2011.
176. T. Akutsu, A. A. Melkman, T. Tamura and M. Yamamoto, Determining a singleton attractor of a Boolean network with nested canalyzing functions,
     Journal of Computational Biology, 18, 1275-1290, 2011.
177. J. C. Nacher and T. Akutsu, On the degree distribution of projected networks mapped from bipartite networks,
     Physica A, 390, 4636-4651, 2011.
178. M. Hayashida, M. Kamada, J. Song and T. Akutsu, Conditional random field approach to prediction of protein-protein interactions using domain information,
     BMC Systems Biology, 5, Suppl. 1, S8 (9 pages), 2011.
     Preliminary version has appeared in ISB 2010.
179. K. Sato, Y. Kato, M. Hamada, T. Akutsu and K. Asai, IPknot: fast and accurate prediction of RNA secondary structures with pseudoknots using integer programming,
     Bioinformatics (Suppl. for ISMB/ECCB 2011), 27, i85-i93, 2011.
180. T. Imada, S. Ota, H. Nagamochi and T. Akutsu, Efficient enumeration of stereoisomers of tree structured molecules using dynamic programming,
     Journal of Mathematical Chemistry, 49, 910-970, 2011.
181. D. Fukagawa, T. Tamura, A. Takasu, E. Tomita and T. Akutsu, A clique-based method for the edit distance between unordered trees and its application to analysis of glycan structures,
     BMC Bioinformatics, 12, Suppl 1 (Suppl. for APBC 2011), S14 (9 pages), 2011.
182. J. Song, H. Tan, S. E. Boyd, H. Shen, K. Mahmood, G. I. Webb, T. Akutsu, J. C. Whisstock and R. N. Pike, Bioinformatic approaches for predicting substrates of proteases (Invited Review Paper),
     Journal of Bioinformatics and Computational Biology, 9, 149-178, 2011.
183. T. Akutsu, D. Fukagawa, A. Takasu and T. Tamura, Exact algorithms for computing tree edit distance between unordered trees,
     Theoretical Computer Science, 412, 352-364, 2011.
184. J.B. Brown, T. Urata, T. Tamura, M. A. Arai, T. Kawabata and T. Akutsu, Compound analysis via graph kernels incorporating chirality,
     Journal of Bioinformatics and Computational Biology, 8, Suppl. 1 (Suppl. for GIW 2010), 63-81, 2010.
185. Y. Zhao, M. Hayashida and T. Akutsu, Integer programming-based method for grammar-based tree compression and its application to pattern extraction of glycan tree structures,
     BMC Bioinformatics, 11, Suppl. 11 (Suppl. for GIW 2010), S4 (11 pages), 2010.
186. Y. Kato, K. Sato, M. Hamada, Y. Watanabe, K. Asai and T. Akutsu, RactIP: fast and accurate prediction of RNA-RNA interaction using integer programming,
     Bioinformatics (Suppl. for ECCB 2010), 26, i460-i466, 2010.
187. M. Hayashida and T. Akutsu, Comparing biological networks via graph compression,
     BMC Systems Biology, 4, Suppl. 2, S13 (11 pages), 2010.
     Preliminary version has appeared in OSB 2009.
188. T. Akutsu, A bisection algorithm for grammar-based compression of ordered trees,
     Information Processing Letters, 110, 815-820, 2010.
189. J. C. Nacher, M. Hayashida and T. Akutsu, The role of internal duplication in the evolution of multi-domain proteins,
     BioSystems, 101, 127-135, 2010.
190. T. Tamura and T. Akutsu, Exact algorithms for finding a minimum reaction cut under a Boolean model of metabolic networks,
     IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E93-A, 1497-1507, 2010.
191. A. A. Melkman, T. Tamura and T. Akutsu, Determining a singleton attractor of an AND/OR Boolean network in O(1.587^n) time,
     Information Processing Letters, 110, 565-569, 2010.
192. Y. Ishida, Y. Kato, L. Zhao, H. Nagamochi and T. Akutsu, Branch-and-bound algorithms for enumerating treelike chemical graphs with given path frequency using detachment-cut,
     Journal of Chemical Information and Modeling, 50, 934-946, 2010.
193. J. Song, K. Takemoto, H. Shen, H. Tan, M. M. Gromiha and T. Akutsu, Prediction of protein folding rates from structural topology and complex network properties,
     IPSJ Transactions on Bioinformatics, 3, 40-53, 2010.
194. J. Song, H. Tan, H. Shen, K. Mahmood. S. E. Boyd, G. I. Webb, T. Akutsu and J. C. Whisstock, Cascleave: towards more accurate prediction of caspase substrate cleavage sites,
     Bioinformatics, 26, 752-760, 2010.
195. T. Akutsu, Tree edit distance problems: algorithms and applications to bioinformatics (Invited Survey Paper),
     IEICE Transactions on Information and Systems, E93-D, 208-218, 2010.
196. T. Tamura, K. Takemoto and T. Akutsu, Finding minimum reaction cuts of metabolic networks under a Boolean model using integer programming and feedback vertex sets,
     International Journal of Knowledge Discovery in Bioinformatics, 1, 14-31, 2010.
     Preliminary version has appeared in IIBM 2009.
197. T. Akutsu, D. Fukagawa and A. Takasu, Approximating tree edit distance through string edit distance,
     Algorithmica, 57, 325-348, 2010.
     Preliminary version has appeared in ISAAC 2006.
198. M. Hayashida, T. Tamura, T. Akutsu, W-K. Ching and Y. Cong, Distribution and enumeration of attractors in probabilistic Boolean networks,
     IET Systems Biology, 3, 465-474, 2009.
     Preliminary version has appeared in OSB 2008.
199. J. C. Nacher, T. Ochiai, M. Hayashida and T. Akutsu, A mathematical model for generating bipartite graphs and its application to protein networks,
     Journal of Physics A: Mathematical and Theoretical, 42, 485005 (10pages), 2009.
     Preliminary version has appeared in COMPLEX 2009.
200. J. Song, H. Tan, K. Mahmood, R. H. P. Law, A. M. Buckle, G. I. Webb, T. Akutsu and J. C. Whisstock, Prodepth: predict residue depth by support vector regression approach from sequences only,
     PLoS ONE, 4, e7072 (14 pages), 2009.
201. T. Akutsu and T. Tamura, On finding a fixed point in a Boolean network with maximum indegree 2,
     IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E92-A, 1771-1778, 2009.
202. K. Mouri, J. C. Nacher and T. Akutsu, A mathematical model for the detection mechanism of DNA double-strand breaks depending on autophosphorylation of ATM,
     PLoS ONE, 4, e5131 (14 pages), 2009.
203. Y. Kato, T. Akutsu and H. Seki, Dynamic programming algorithms and grammatical modeling for protein beta-sheet prediction,
     Journal of Computational Biology, 16, 945-957, 2009.
     Preliminary version has appeared in PRIB 2008.
204. T. Tamura and T. Akutsu, Algorithms for singleton attractor detection in planar and nonplanar AND/OR Boolean networks,
     Mathematics in Computer Science, 2, 401-420, 2009.
     Preliminary version has appeared in AB 2008.
205. W.-K. Ching, S.-Q. Zhang, Y. Jiao, T. Akutsu, N.-K. Tsing and A.S. Wong, Optimal control policy for probabilistic Boolean networks with hard constraints,
     IET Systems Biology, 3, 90-99, 2009.
     Preliminary version has appeared in OSB 2007.
206. T. Tamura and T. Akutsu, Detecting a singleton attractor in a Boolean network utilizing SAT algorithms,
     IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E92-A, 493-501, 2009.
     Preliminary version has appeared in FCT 2007.
207. U. Poolsap, Y. Kato and T. Akutsu, Prediction of RNA secondary structure with pseudoknots using integer programming,
     BMC Bioinformatics, 10 (Supplement for APBC 2009), S38 (11 pages), 2009.
208. Y. Kato, T. Akutsu and H. Seki, A grammatical approach to RNA-RNA interaction prediction,
     Pattern Recognition, 42, 531-538, 2009.
     Preliminary version has appeared in CMLS 2007.
209. J.C. Nacher, M. Hayashida and T. Akutsu, Emergence of scale-free distribution in protein-protein interaction networks based on random selection of interacting domain pairs,
     BioSystems, 95, 155-159, 2009.
210. J. B. Brown and T. Akutsu, Identification of novel DNA repair proteins via primary sequence, secondary structure, and homology,
     BMC Bioinformatics, 10, 25, 2009.
211. T. Akutsu, M. Hayashida, S-Q. Zhang, W-K. Ching and M. K. Ng, Analyses and algorithms for predecessor and control problems for Boolean networks of bounded indegree,
     IPSJ Transactions on Bioinformatics, 1, 23-34, 2008.
     Preliminary version has appeared in GENSIPS 2007.
212. T. Akutsu, D. Fukagawa and A. Takasu, Improved approximation of the largest common subtree of two unordered trees of bounded height,
     Information Processing Letters, 109, 165-170, 2008.
213. M. Hayashida, F. Sun, S. Aburatani, K. Horimoto and T. Akutsu, Integer programming-based approach to allocation of reporter genes for cell array analysis,
     International Journal of Bioinformatics Research and Applications, 4, 385-399, 2008.
     Preliminary version has appeared in OSB 2007.
214. K. Takemoto and T. Akutsu, Origin of structural difference in metabolic networks with respect to temperature,
     BMC Systems Biology, 2, 82 (13 pages), 2008.
215. M. Hayashida, T. Tamura, T. Akutsu, S-Q. Zhang and W-K. Ching, Algorithms and complexity analyses for control of singleton attractors in Boolean networks,
     EURASIP Journal on Bioinformatics and Systems Biology, 2008, 521407 (16pages), 2008.
216. H. Fujiwara, J. Wang, L. Zhao, H. Nagamochi and T. Akutsu, Enumerating tree-like chemical graphs with given path frequency,
     Journal of Chemical Information and Modeling, 48, 1345-1357, 2008.
217. J. Song, H. Tan, K. Takemoto and T. Akutsu, HSEpred: predict half-sphere exposure from protein sequences,
     Bioinformatics, 24, 1489-1497, 2008.
218. M. Hayashida, T. Akutsu and H. Nagamochi, A clustering method for analysis of sequence similarity networks of proteins using maximal components of graphs,
     IPSJ Transactions on Bioinformatics, 49-Sig 5 (TBIO 4), 15-24, 2008.
     Preliminary version has appeared in APBC 2007.
219. T. Tamura and T. Akutsu, Subcellular location prediction of proteins using support vector machines with alignment of block sequences utilizing amino acid composition,
     BMC Bioinformatics, 8, 466, 2007.
220. K. Takemoto, J.C. Nacher and T. Akutsu, Correlation between structure and temperature in prokaryotic metabolic networks,
     BMC Bioinformatics, 8, 303, 2007.
221. J.C. Nacher and T. Akutsu, Recent progress on the analysis of power-law features in complex cellular networks (Review Paper),
     Cell Biochemistry and Biophysics, 49 37-47, 2007.
222. T. Ochiai, J.C. Nacher and T. Akutsu, Emergence of the self-similar property in gene expression dynamics,
     Physica A, 382, 739-752, 2007.
223. W-K. Ching, S-Q. Zhang, M.K. Ng and T. Akutsu, An approximation method for solving the steady-state probability distribution of probabilistic Boolean networks,
     Bioinformatics, 23, 1511-1518, 2007.
224. K. Takemoto, C. Oosawa and T. Akutsu, Structure of n-clique networks embedded in a complex network,
     Physica A, 380, 665-672, 2007.
225. S-Q. Zhang, M. Hayashida, T. Akutsu, W-K. Ching and M. K. Ng, Algorithms for finding small attractors in Boolean networks,
     EURASIP Journal on Bioinformatics and Systems Biology, 2007, 20180 (13 pages), 2007.
226. T. Tamura and T. Akutsu, Approximation algorithms for optimal RNA secondary structures common to multiple sequences,
     IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E90-A, 917-923, 2007.
227. T. Akutsu, H. Bannai, S. Miyano and S. Ott, On the complexity of deriving position specific score matrices from positive and negative sequences,
     Discrete Applied Mathematics, 155, 676-685, 2007.
     Preliminary version has appeared in CPM 2002.
228. T. Akutsu, H. Arimura and S. Shimozono, Hardness results on local multiple alignment of biological sequences,
     IPSJ Transactions on Bioinformatics, 48-Sig 5 (TBIO 2), 30-38, 2007.
     Preliminary results have appeared in RECOMB 2000.
229. T. Akutsu, M. Hayashida, W-K. Ching and M.K. Ng, Control of Boolean networks: Hardness results and algorithms for tree structured networks,
     Journal of Theoretical Biology, 244, 670-679, 2007.
     Preliminary version has appeared in APBC 2006.
230. S-Q. Zhang, W-K. Ching, M.K. Ng and T. Akutsu, Simulation study in Probabilistic Boolean Network models for genetic regulatory networks,
     International Journal of Data Mining and Bioinformatics, 1, 217-240, 2007.
231. J.C. Nacher and T. Akutsu, Sensitivity of the power-law exponent in gene expression distribution to mRNA decay rate,
     Physics Letters A, 360, 174-178, 2006.
232. T. Akutsu, A relation between edit distance for ordered trees and edit distance for Euler strings,
     Information Processing Letters, 100, 105-109, 2006.
233. M.K. Ng, S-Q. Zhang, W-K. Ching and T. Akutsu, A control model for Markovian genetic regulatory networks,
     Transactions on Computational Systems Biology V (Lecture Notes in Bioinformatics 4070), 36-48, 2006.
234. J.C. Nacher, J-M. Schwartz, M. Kanehisa and T. Akutsu, Identification of metabolic units induced by environmental signals,
     Bioinformatics (Proc. ISMB 2006), 22, e375-e383, 2006.
235. T. Akutsu, Algorithms for point set matching with k-differences,
     International Journal of Foundations of Computer Science, 17, 903-917, 2006.
     Preliminary version has appeared in COCOON 2004.
236. D. Fukagawa and T. Akutsu, Fast algorithms for comparison of similar unordered trees,
     International Journal of Foundations of Computer Science, 17, 703-729, 2006.
     Preliminary version has appeared in ISAAC 2004.
237. J.C. Nacher, M. Hayashida and T. Akutsu, Protein domain networks: Scale-free mixing of positive and negative exponents,
     Physica A, 367, 538-552, 2006.
238. H. Saigo, J-P. Vert and T. Akutsu, Optimizing amino acid substitution matrices with a local alignment kernel,
     BMC Bioinformatics, 7, 246, 2006.
239. T. Akutsu, Recent advances in RNA secondary structure prediction with pseudoknots (Review Paper),
     Current Bioinformatics, 1, 115-129, 2006.
240. Dukka Bahadur K.C., E. Tomita, J. Suzuki, K. Horimoto and T. Akutsu, Protein threading with profiles and distance constraints using clique based algorithms,
     Journal of Bioinformatics and Computational Biology, 4, 19-42, 2006.
     Preliminary version has appeared in APBC 2005.
241. T. Akutsu, M. Hayashida, Dukka Bahadur K.C., E. Tomita, J. Suzuki and K. Horimoto, Dynamic programming and clique based approaches for protein threading with profiles and constraints,
     IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E89-A, 1215-1222, 2006.
     Preliminary version has appeared in BIBE 2004.
242. J.C. Nacher, T. Ochiai, T. Yamada, M. Kanehisa and T. Akutsu, The role of log-normal dynamics in the evolution of biochemical pathways,
     BioSystems, 83, 26-37, 2006.
243. S. Matsuda, J-P. Vert, H. Saigo, N. Ueda, H. Toh and T. Akutsu, A novel representation of protein sequences for prediction of subcellular location using support vector machines,
     Protein Science, 14, 2804-2813, 2005.
244. J.C. Nacher, T. Ochiai and T. Akutsu, On the relation between fluctuations and scaling-law in gene expression time series from yeast to human,
     Modern Physics Letters B, 19, 1169-1177, 2005.
245. W-K. Ching, M.K. Ng, E.S. Fung and T. Akutsu, On construction of stochastic genetic networks based on gene expression sequences,
     International Journal of Neural Systems, 15, 297-310, 2005.
246. P. Mahe, N. Ueda, T. Akutsu, J-L. Perret and J-P. Vert, Graph kernels for molecular structure-activity relationship analysis with support vector machines,
     Journal of Chemical Information and Modeling, 45, 939-951, 2005.
     Preliminary version has appeared in ICML 2004.
247. T. Ochiai, J.C. Nacher and T. Akutsu, A stochastic approach to multi-gene expression dynamics,
     Physics Letters A, 339, 1-9, 2005.
248. N. Ueda, K.F. Aoki-Kinoshita, A. Yamaguchi, T. Akutsu and H. Mamitsuka, A probabilistic model for mining labeled ordered trees: capturing patterns in carbohydrate sugar chains,
     IEEE Transactions on Knowledge and Data Engineering, 17, 1051-1064, 2005.
249. K.F. Aoki, H. Mamitsuka, T. Akutsu and M. Kanehisa, A score matrix to reveal the hidden links in glycans,
     Bioinformatics, 21, 1457-1463, 2005.
250. J.C. Nacher, N. Ueda, M. Kanehisa and T. Akutsu, Flexible construction of hierarchical scale-free networks with general exponent,
     Physical Review E, 71, 036132(1-7), 2005.
251. M. Itoh, S. Goto, T. Akutsu and M. Kanehisa, Fast and accurate database homology search using upper bounds of local alignment scores,
     Bioinformatics, 21, 912-921, 2005.
252. Dukka Bahadur K.C., E. Tomita, J. Suzuki and T. Akutsu, Protein side-chain packing problem: a maximum edge-weight clique algorithmic approach,
     Journal of Bioinformatics and Computational Biology, 3, 103-126, 2005.
     Preliminary version has appeared in APBC 2004.
253. J.C. Nacher, T. Yamada, S. Goto, M. Kanehisa and T. Akutsu, Two complementary representations of a scale-free network,
     Physica A, 349, 349-363, 2005.
254. M. Hayashida, N. Ueda and T. Akutsu, A fast method for inferring strengths of protein-protein interactions and a hardness result,
     The IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences (Japanese Edition), J88-A, 83-90, 2005.
     Preliminary version has appeared in IBSB 2004.
255. D. Fukagawa and T. Akutsu, Performance analysis of a greedy algorithm for inferring Boolean functions,
     Information Processing Letters, 93, 7-12, 2005.
     Preliminary version has appeared in DS 2003.
256. J.C. Nacher, N. Ueda, T. Yamada, M. Kanehisa and T. Akutsu, Clustering under the line graph transformation: application to reaction network, BMC Bioinformatics, 5, 207, 2004.
257. T. Ochiai, J.C. Nacher and T. Akutsu, A constructive approach to gene expression dynamics,
     Physics Letters A, 330, 313-321, 2004.
258. K.F. Aoki, N. Ueda, A. Yamaguchi, M. Kanehisa, T. Akutsu and H. Mamitsuka, Application of a new probabilistic model for recognizing complex patterns in glycans,
     Bioinformatics, 20, Suppl. 1 (Proc. ISMB/ECCB 2004), i6-i14, 2004.
259. K.F. Aoki, A. Yamaguchi, N. Ueda, T. Akutsu, H. Mamitsuka, S. Goto and M. Kanehisa, KCaM (KEGG Carbohydrate Matcher): a software tool for analyzing the structures of carbohydrate sugar chains,
     Nucleic Acids Research, 32, Web Server Issue, w267-w272, 2004.
260. H. Saigo, J-P. Vert, N. Ueda and T. Akutsu, Protein homology detection using string alignment kernels,
     Bioinformatics, 20, 1682-1689, 2004.
261. K.F. Aoki, N. Ueda, A. Yamaguchi, T. Akutsu, M. Kanehisa and H. Mamitsuka, Managing and analyzing carbohydrate data,
     ACM SIGMOD Record (Magazine), 33, 2004.
262. T. Akutsu, Efficient extraction of mapping rules of atoms from enzymatic reaction data,
     Journal of Computational Biology, 11, 449-462, 2004.
     Preliminary version has appeared in RECOMB 2003.
263. Y. Hourai, T. Akutsu and Y. Akiyama, Optimizing substitution matrices by separating score distributions,
     Bioinformatics, 20, 863-873, 2004.
264. M. Hayashida, N. Ueda and T. Akutsu, Inferring strengths of protein-protein interactions from experimental data using linear programming,
     Bioinformatics, 19, ii58-ii65, 2003.
265. D. Shinozaki, T. Akutsu and O. Maruyama, Finding optimal degenerated patterns in DNA sequences,
     Bioinformatics, 19, ii206-ii214, 2003.
266. T. Akutsu, S. Kuhara, O. Maruyama and S. Miyano, Identification of genetic networks by strategic gene disruptions and gene overexpressions under a boolean model,
     Theoretical Computer Science, 298, 235-251, 2003.
     Preliminary version has appeared in SODA'98.
267. T. Akutsu, K. Kanaya, A. Ohyama and A. Fujiyama, Point matching under non-uniform distortions,
     Discrete Applied Mathematics, 127, 5-21, 2003.
     Preliminary version has appeared in CPM'99.
268. T. Akutsu, S. Miyano and S. Kuhara, A simple greedy algorithm for finding functional relations: efficient implementation and average case analysis,
     Theoretical Computer Science, 292, 481-495, 2003.
     Preliminary version has appeared in DS 2000.
269. T. Akutsu, S. Miyano and S. Kuhara, Algorithms for identifying Boolean networks and related biological networks based on matrix multiplication and fingerprint function,
     Journal of Computational Biology, 7, 331-343, 2000.
270. T. Akutsu, S. Miyano and S. Kuhara, Inferring qualitative relations in genetic networks and metabolic pathways,
     Bioinformatics, 16, 727-734, 2000.
271. T. Akutsu, Dynamic programming algorithms for RNA secondary structure prediction with pseudoknots,
     Discrete Applied Mathematics, 104, 45-62, 2000.
272. T. Akutsu and M. M. Halldorsson, On the approximation of largest common subtrees and largest common point sets,
     Theoretical Computer Science, 233, 33-50, 2000.
     Preliminary version has appeared in ISAAC'94.
273. T. Akutsu, Approximation and exact algorithms for RNA secondary structure prediction and recognition of stochastic context-free languages,
     Journal of Combinatorial Optimization, 3, 321-336, 1999.
     Preliminary version has appeared in ISAAC'98.
274. T. Akutsu and S. Miyano, On the approximation of protein threading,
     Theoretical Computer Science 210, 261-275, 1999.
     Preliminary version has appeared in RECOMB '97.
275. T. Akutsu, H. Tamaki and T. Tokuyama, Distribution of distances and triangles in a point set and algorithms for computing the largest common point sets,
     Discrete and Computational Geometry, 20, 307-331, 1998.
     Preliminary version has appeared in ACM Symp. Computational Geometry.
276. T. Akutsu, On determining the congruence of point sets in d dimensions,
     Computational Geometry: Theory and Applications, 9, 247-256, 1998.
     Preliminary version has appeared as On determining the congruity of point sets in higher dimensions in ISAAC'94.
277. T. Akutsu, K. Onizuka and M. Ishikawa, Rapid protein fragment search using hash functions based on the Fourier transform (Short Paper),
     Computer Applications in the Biosciences (CABIOS) 13, 357-364, 1997.
     Preliminary version has appeared in HICSS-28.
278. T. Akutsu, Protein structure alignment using dynamic programming and iterative improvement,
     IEICE Trans. Information and Systems E79-D, 1629-1636, 1996.
279. T. Akutsu, Approximate string matching with variable length don't care characters,
     IEICE Trans. Information and Systems E79-D, 1353-1354, 1996 (LETTER).
280. T. Akutsu, Approximate string matching with don't care characters,
     Information Processing Letters 55, 235-239, 1995.
     Preliminary version has appeared in CPM'94.
281. T. Akutsu, A parallel algorithm for determining the congruence of point sets in three-dimensions,
     IEICE Trans. Information and Systems E78-D, 321-325, 1995.
282. T. Akutsu and A. Takasu, On PAC learnability of functional dependencies,
     New Generation Computing 12, 359-374, 1994.
     Preliminary version has appeared in ALT'92.
283. T. Akutsu, A linear time pattern matching algorithm between a string and a tree,
     IEICE Trans. Information and Systems E77-D, 281-287, 1994.
     Preliminary version has appeared in CPM'93.
284. T. Akutsu, A polynomial time algorithm for finding a largest common subgraph of almost trees of bounded degree,
     IEICE Trans. Fundamentals, E76-A, 1488-1493, 1993.
285. T. Akutsu, S.Kobayashi, K.Hori and S.Ohsuga, Algorithms for finding the largest subtree whose copies cover all the leaves,
     IEICE Trans. Information and Systems, E76-D, 707-710, 1993 (LETTER).
286. A. Takasu and T. Akutsu, A minimum path decomposition of the Hasse diagram for testing the consistency of functional dependencies,
     IEICE Trans. Information and Systems, E76-D, 299-301, 1993 (LETTER).
287. E. Suzuki, T. Akutsu and S. Ohsuga, Knowledge-based system for computer-aided drug design,
     Knowledge-Based Systems, 6, 114-126, 1993.
288. T. Akutsu, An RNC algorithm for finding a largest common subtree of two trees,
     IEICE Trans. Information and Systems, E75-D, 95-101, 1992.
289. T.Akutsu, An NC algorithm for computing canonical forms of graphs of bounded separator,
     IEICE Trans. Fundamentals, E75-A, 512-514, 1992 (LETTER).
290. T. Akutsu, An $O(n^2)$ time algorithm for computing a canonical form of a chemical structure which has a planar graph structure,
     Transactions of Information Processing Society of Japan, 33, 1487-1496, 1992 (in Japanese).
291. T. Akutsu, A new method of computer representation of stereochemistry. Transforming a stereochemical structure into a graph,
     Journal of Chemical Information and Computer Sciences, 31, 414-417, 1991.
292. T. Akutsu, E. Suzuki and S. Ohsuga, Logic-based approach to expert systems in chemistry,
     Knowledge-Based Systems, 4, 103-116, 1991.
293. T. Akutsu, C. Li, E. Suzuki and S. Ohsuga, Development of a tool for building expert systems in organic chemistry, Transactions of Information Processing Society of Japan, 32, 425-434, 1991 (in Japanese).
294. T. Akutsu and S. Ohsuga, Some properties of Prolog,
     Journal of Japanese Society for Artificial Intelligence, 2, 223-233, 1987 (in Japanese).

Conference Papers

1. T. Akutsu, T. Mori, N. Nakamura, S. Kozawa, Y. Ueno, and T. N. Sato, On the complexity of tree edit distance with variables,
   Proc. 33rd International Symposium on Algorithms and Computation (ISAAC 2022), 44 (14 pages), 2022.
2. J. Zhu, K. Haraguchi, H. Nagamochi, and T. Akutsu, Adjustive linear regression and its application to the inverse QSAR (short paper),
   Proc. 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022), 144-151, 2022.
3. J. Zhu, N. A. Azam, K. Haraguchi, L. Zhao, H. Nagamochi, and T. Akutsu, A method for molecular design based on linear regression and integer programming,
   Proc. 12th International Conference on Bioscience Biochemistry and Bioinformatics (ICBBB '22), 8 pages, 2022.
4. N. A. Azam, J. Zhu, K. Haraguchi, L. Zhao, H. Nagamochi, and T. Akutsu, Molecular design based on artificial neural networks, integer programming and grid neighbor search (short paper),
   Proc. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2021), 360-363, 2021.
5. K. Tanaka, J. Zhu, N. A. Azam, K. Haraguchi, L. Zhao, H. Nagamochi, and T. Akutsu, An inverse QSAR method based on decision tree and integer programming,
   Proc. 17h International Conference on Intelligent Computing (ICIC 2021), Lecture Notes in Computer Science 12837, 628-644, 2021.
6. J. Zhu, N. A. Azam, K. Haraguchi, L. Zhao, H. Nagamochi, and T. Akutsu, An improved integer programming formulation for inferring chemical compounds with prescribed topological structures,
   Proc. 34th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE 2021), 197-209, 2021.
7. R. Ito, N. A. Azam, C. Wang, A. Shurbevski, H. Nagamochi, and T. Akutsu, A novel method for the inverse QSAR/QSPR to monocyclic chemical compounds based on artificial neural networks and integer programming,
   Proc. 21st International Conference on Bioinformatics & Computational Biology, 2020.
8. F. Zhang, J. Zhu, R. Chiewvanichakorn, A. Shurbevski, H. Nagamochi, and T. Akutsu, A new integer linear programming formulation to the inverse QSAR/QSPR for acyclic chemical compounds using skeleton trees,
   Proc. 33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE 2020), Lecture Notes in Computer Science 12144, 433-444, 2020.
9. R. Chiewvanichakorn, C. Wang, Z. Zhang, A. Shurbevski, H. Nagamochi, and T. Akutsu, A method for the inverse QSAR/QSPR based on artificial neural networks and mixed integer linear programming,
   Proc. 2020 10th International Conference on Bioscience, Biochemistry and Bioinformatics (ICBBB 2020), 40-46, 2020.
10. J. Zeng, H. Cai, and T. Akutsu, Breast cancer subtype by imbalanced omics data through a deep learning fusion model,
    Proc. 2020 10th International Conference on Bioscience, Biochemistry and Bioinformatics (ICBBB 2020), 78-83, 2020.
11. N. A. Azam, R. Chiewvanichakorn, F. Zhang, A. Shurbevski, H. Nagamochi, and T. Akutsu, A novel method for the inverse QSAR/QSPR based on artificial neural networks and mixed integer linear programming with guaranteed admissibility (short paper),
    Proc. 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020), 101-108, 2020.
12. T. Akutsu and H. Nagamochi, A mixed integer linear programming formulation to artificial neural networks,
    Proc. 2nd International Conference on Information Science and System (ICISS 2019), 215-220, 2019.
13. T. Akutsu, J. Jansson, R. Li, A. Takasu and T. Tamura, New and improved algorithms for unordered tree inclusion,
    Proc. 29th International Symposium on Algorithms and Computation (ISAAC 2018), 27:1-27:12, 2018.
14. T. Matsubara, J. C. Nacher, T. Ochiai, M. Hayashida and T, Akutsu, Convolutional neural network approach to lung cancer classification integrating protein interaction network and gene expression profiles (short paper),
    International Workshop on Cancer Bioinformatics and Intelligent Medicine 2018 (CBIM 2018), in Proc. 18th IEEE International Conference on BioInformatics and BioEngineering (BIBE 2018), 151-154, 2018.
15. C-Y, Lin, R. Li, T. Akutsu, P. Ruan, S. Sea and J-M. Yang, Deep learning with evolutionary and genomic profiles for identifying cancer subtypes (short paper),
    International Workshop on Cancer Bioinformatics and Intelligent Medicine 2018 (CBIM 2018), in Proc. 18th IEEE International Conference on BioInformatics and BioEngineering (BIBE 2018), 147-150, 2018.
16. T. Akutsu, C. de la Higuera and T. Tamura, A simple linear-time algorithm for computing the centroid and canonical form of a plane graph and its applications,
    Proc. 29th Annual Symposium on Combinatorial Pattern Matching (CPM 2018), 10:1-10:12, 2018.
17. Y. Tamura, A. Shurbevski, H. Nagamochi and T. Akutsu, Enumerating chemical mono-block 3-augmented trees with two junctions,
    Proc. 8th International Conference on Bioscience, Biochemistry and Bioinformatics (ICBBB 2018), 48-55, 2018.
18. M. Hayashida, H. Koyano and T. Akutsu, Grammar-based compression for directed and undirected generalized series-parallel graphs using integer linear programming (short paper),
    Proc. 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 3: BIOINFORMATICS, 105-111, 2018.
19. J. Jindalertudomdee, M. Hayashida, J. Song and T. Akutsu, Host-pathogen protein interaction prediction based on local topology structures of a protein interaction network,
    Proc. IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE 2016), 7-12, 2016.
20. T. Tamura, C-Y. Lin, J-M. Yang and T. Akutsu, Finding influential genes using gene expression data and Boolean models of metabolic networks,
    Proc. IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE 2016), 57-63, 2016.
21. F. He, A. Hanai, H. Nagamochi and T. Akutsu, Enumerating naphthalene isomers of tree-like chemical graphs (Short Paper),
    Proc. 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 3: BIOINFORMATICS, 258-265, 2016.
22. Y. Qiu, X. Cheng, W-K. Ching, H. Jiang, T. Akutsu, On observability of attractors in Boolean networks (short paper),
    Proc. 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2015), 263-266, 2015.
23. T. Akutsu, J. Jansson, A. Takasu and T. Tamura, On the parameterized complexity of associative and commutative unification,
    Post Proc. 9th International Symposium on Parameterized and Exact Computation (IPEC 2014), Lecture Notes in Computer Science 8894, 15-27, 2014.
24. M. Hayashida, H. Koyano and T. Akutsu, Measuring the similarity of protein structures using image local feature descriptors SIFT and SURF,
    Proc. 8th International Conference on Systems Biology and 4th Translational Bioinformatics Conference (ISB/TBC 2014), 167-171, 2014.
25. J.C. Nacher and T. Akutsu, Analysis on critical nodes in controlling complex networks using dominating sets,
    Second Workshop on Complex Networks and their Applications,
    (A Part of Proc. 2013 Int. Conf. Signal-Image Technology & Internet-Based Systems (SITIS 2013)), 649-654, 2013.
26. T. Akutsu, T. Tamura, A. A. Melkman and A. Takasu, On the complexity of finding a largest common subtree of bounded degree,
    Proc. 19th Int. Symp. Fundamentals of Computation Theory (FCT 2013), Lecture Notes in Computer Science 8070, 4-15, 2013.
27. Y. Sakuma, M. Kamada, M. Hayashida and T. Akutsu, Inferring strengths of protein-protein interactions using support vector regression,
    Proc. 2013 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA2013), 2013.
28. N. Nakajima and T. Akutsu, Network completion for time varying genetic networks,
    The 6th International Workshop on Intelligent Informatics in Biology and Medicine (IIBM 2013),
    (A Part of Proc. 7th International Conference on Complex, Intelligent and Software Intensive Systems (CISIS-2013)), 553-558, 2013.
29. J. C. Nacher and T. Akutsu, Analysis on controlling complex networks based on dominating sets,
    Journal of Physics: Conference Series 410, 012104, 2013.
    (IC-MSQUARE 2012: International Conference on Mathematical Modelling in Physical Sciences)
30. T. Akutsu and T. Tamura, On the complexity of the maximum common subgraph problem for partial k-trees of bounded degree,
    Proc. 23rd International Symposium on Algorithms and Computation (ISAAC 2012), Lecture Notes in Computer Science 7676, 146-155, 2012.
31. T. Akutsu, M. Hayashida and T. Tamura, Finding conserved regions in protein structures using support vecto rmachines and structure alignment,
    Proc. 7th IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB2012), Lecture Notes in Bioinformatics 7632, 233-242, 2012.
32. T. Akutsu and T. Tamura, A polynomial-time algorithm for computing the maximum common subgraph of outerplanar graphs of bounded degree,
    Proc. 37th International Symposium on Mathematical Foundations of Computer Science (MFCS 2012), Lecture Notes in Computer Science 7464, 76-87, 2012.
33. M. Hayashida, M. Kamada, J. Song and T. Akutsu, Predicting protein-RNA residue-base contacts using two-dimensional conditional random field,
    Proc. 6th IEEE International Conference on Systems Biology (ISB 2012), 152-157, 2012.
34. T. Akutsu, T. Tamura, D. Fukagawa and A. Takasu, Efficient exponential time algorithms for edit distance between unordered trees,
    Proc. 23rd Annual Symposium on Combinatorial Pattern Matching (CPM 2012), Lecture Notes in Computer Science 7354, 360-372, 2012.
35. M. Kamada, M. Hayashida, J. Song and T. Akutsu, Discriminative random field approach to prediction of protein residue contacts,
    Proc. 5th IEEE International Conference on Systems Biology (ISB 2011), 285-291, 2011.
36. M. Wang, H-B. Shen, T. Akutsu and J. Song, Predicting functional impact of single amino acid polymorphisms by integrating sequence and structural features.
    Proc. 5th IEEE International Conference on Systems Biology (ISB 2011), 18-26, 2011.
37. T. Akutsu, T. Mori, T. Tamura, D. Fukagawa, A. Takasu and E. Tomita, An improved clique-based method for computing edit distance between unordered trees and its application to comparison of glycan structures,
    The 4th International Workshop on Intelligent Informatics in Biology and Medicine (IIBM 2011),
    A Part of Proc. 5th International Conference on Complex, Intelligent and Software Intensive Systems, 536-540, 2011.
38. X. Chen, T. Akutsu, T. Tamura and W-K. Ching, Finding optimal control policy in probabilistic Boolean networks with hard constraints by using integer programming and dynamic programming,
    Proc. IEEE International Conference on Bioinformatics and Biomedicine 2010 (BIBM 2010), 240-246, 2010.
39. M. Hayashida, P. Ruan and T. Akutsu, A quadsection algorithm for grammar-based image compression,
    Proc. 2nd Int. Conf. Future Generation Information Technology 2010 (FGIT 2010), Lecture Notes in Computer Science 6485, 234-248, 2010.
40. M. Hayashida, M. Kamada, J. Song and T. Akutsu, Conditional random field approach to prediction of protein-protein interactions using mutual information between domains,
    Proc. 4th International Conference on Computational Systems Biology (ISB2010), Lecture Notes in Operations Research 13, 276-284, 2010.
41. A. Takasu, D. Fukagawa and T. Akutsu, A variational Bayesian EM algorithm for tree similarity,
    Proc. 20th International Conference on Pattern Recognition (ICPR 2010), 1056-1059, 2010.
42. T. Tamura, Y. Yamanishi, M. Tanabe, S. Goto, M. Kanehisa, K. Horimoto and T. Akutsu, Integer programming-based method for completing signaling pathways and its application to analysis of colorectal cancer,
    Genome Informatics, 24 (The 10th Int. Workshop on Bioinformatics and Systems Biology), 193-203, 2010.
43. Y. Zhao, T. Tamura, M. Hayashida and T. Akutsu, A dynamic programming algorithm to predict synthesis processes of tree-structured compounds with graph grammar,
    Genome Informatics, 24 (The 10th Int. Workshop on Bioinformatics and Systems Biology), 218-229, 2010.
44. U. Poolsap, Y. Kato and T. Akutsu, Dynamic programming algorithms for RNA structure prediction with binding sites,
    Proc. Pacific Symposium on Biocomputing 2010 (PSB 2010), 98-107, 2010.
45. T. Akutsu, M. Hayashida and T. Tamura, Integer programming-based methods for attractor detection and control of Boolean networks,
    Proc. The combined 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference (IEEE CDC/CCC 2009), 5610-5617, 2009.
46. T. Imada, S. Ota, H. Nagamochi and T. Akutsu, Enumerating stereoisomers of tree structured molecules using dynamic programming,
    Proc. 20th International Symposium on Algorithms and Computation (ISAAC 2009), Lecture Notes in Computer Science 5878, 14-23, 2009.
47. T. Tamura, N. Christian, K. Takemoto, O. Ebenhoeh and T. Akutsu, Analysis and prediction of nutritional requirements using structural properties of metabolic networks and support vector machines,
    Genome Informatics, 22 (The 9th Int. Workshop on Bioinformatics and Systems Biology), 176-190, 2009.
48. Y. Cong, T. Tamura, T. Akutsu and W-K. Ching, Efficient computation of impact degrees for multiple reactions in metabolic networks with cycles (short paper),
    Proc. ACM Third International Workshop on Data and Text Mining in Bioinformatics (DTMBIO 2009), 67-70, 2009.
49. A. Takasu, D. Fukagawa and T. Akutsu, Latent topic extraction from relational table for record matching (regular paper),
    Proc. 12th International Conference on Discovery Science (DS 2009), Lecture Notes in Artificial Intelligence 5808, 449-456, 2009.
50. T. Akutsu, T. Tamura and K. Horimoto, Completing networks using observed data,
    Proc. 20th International Conference on Algorithmic Learning Theory (ALT 2009), Lecture Notes in Artificial Intelligence 5809, 126-140, 2009.
51. M. Hayashida and T. Akutsu, Comparing biological networks via graph compression,
    Proc. 3rd International Symposium on Optimization and Systems Biology (OSB 2009), Lecture Notes in Operations Research 11, 168-176, 2009.
52. D. Fukagawa, T. Akutsu and A. Takasu, Constant factor approximation of edit distance of bounded height unordered trees,
    Proc. 16th International Symposium on String Processing and Information Retrieval (SPIRE 2009), Lecture Notes in Computer Science 5721, 7-17, 2009.
53. T. Tamura, K. Takemoto and T. Akutsu, Measuring structural robustness of metabolic networks under a Boolean model using integer programming and feedback vertex sets,
    The 2nd International Workshop on Intelligent Informatics in Biology and Medicine (IIBM 2009),
    A Part of Proc. 3rd International Conference on Complex, Intelligent and Software Intensive Systems, 819-824, 2009.
54. J. C. Nacher, T. Ochiai, M. Hayashida and T. Akutsu, A bipartite graph based model of protein domain networks,
    Proc. 1st International Conference on Complex Sciences: Theory and Applications (Complex 2009), Part1, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 4, 525-535, 2009.
55. Y. Ishida, L. Zhao, H. Nagamochi and T. Akutsu, Improved algorithms for enumerating tree-like chemical graphs with given path frequency,
    Genome Informatics, 21 (The 19th Int. Conference on Genome Informatics), 53-64, 2008.
56. M. Hayashida, T. Tamura, T. Akutsu and W-K. Ching, On distribution and enumeration of attractors in probabilistic Boolean networks,
    Proc. 2nd International Symposium on Optimization and Systems Biology (OSB 2008), Lecture Notes in Operations Research 9, 91-100, 2008.
57. Y. Kato, T. Akutsu and H. Seki, Prediction of protein beta-sheets: dynamic programming versus grammatical approach,
    Proc. 3rd IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB 2008), Lecture Notes in Bioinformatics 5265, 66-77, 2008.
58. T. Akutsu, M. Hayashida and T. Tamura, Algorithms for inference, analysis and control of Boolean networks (non-refereed tutorial paper),
    Proc. 3rd International Conference on Algebraic Biology (AB 2008), Lecture Notes in Computer Science 5147, 1-15, 2008.
59. T. Tamura and T. Akutsu, An improved algorithm for detecting a singleton attractor in a Boolean network consisting of AND/OR nodes,
    Proc. 3rd International Conference on Algebraic Biology (AB 2008), Lecture Notes in Computer Science 5147, 216-229, 2008.
60. M. Hayashida and T. Akutsu, Image compression-based approach to measuring the similarity of protein structures,
    Proc. 6th Asia-Pacific Bioinformatics Conference (APBC 2008), 221-230, 2008.
61. Y. Kato, T. Akutsu and H. Seki, A grammatical approach to RNA-RNA interaction prediction,
    Proc. 2007 International Symposium on Computational Models for Life Sciences (CMLS 2007), 197-206. 2007.
62. A. Takasu, D. Fukagawa and T. Akutsu, Statistical learning algorithm for tree similarity (short paper),
    Proc. the Seventh IEEE International Conference on Data Mining (ICDM 2007), 667-672, 2007.
63. T. Tamura and T. Akutsu, An O(1.787^n)-time algorithm for detecting a singleton attractor in a Boolean network consisting of AND/OR nodes,
    Proc. 16th International Symposium on Fundamentals of Computation Theory (FCT 2007), Lecture Notes in Computer Science 4639, 494-505, 2007.
64. M. Hayshida, F. Sun, S. Aburatani, K. Horimoto and T. Akutsu, Integer programming-based approach to allocation of reporter genes for cell array analysis,
    Proc. International Symposium on Optimization and Systems Biology (OSB 2007), Lecture Notes in Operations Research 7, 21-28, 2007.
65. W-K. Ching, S-Q. Zhang, Y. Jiao, T. Akutsu and A.S. Wong, Optimal finite-horizon control for probabilistic Boolean networks with hard constraints,
    Proc. International Symposium on Optimization and Systems Biology (OSB 2007), Lecture Notes in Operations Research 7, 288-301, 2007.
66. T. Akutsu, M. Hayashida, S-Q. Zhang, W-K. Ching and M. K. Ng, Finding incoming global states in Boolean networks,
    Proc. 5th IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS07) (appeared as a peer reviewed poster paper).
67. J. Wang, L. Zhao, H. Nagamochi and T. Akutsu, An efficient algorithm for generating colored outerplanar graphs,
    Proc. 4th Annual Conference on Theory and Applications of Models of Computation (TAMC07), Lecture Notes in Computer Science 4484, 573-583, 2007.
68. J.C. Nacher, M. Hayashida and T. Akutsu, Topological aspects of protein networks,
    PostProc. Workshop on Emergent Intelligence on Networked Agents (WEIN06), Studies in Computational Intelligence 56, 147-158, 2007.
69. T. Akutsu and D. Fukagawa, Inferring a chemical structure from a feature vector based on frequency of labeled paths and small fragments,
    Proc. 5th Asia-Pacific Bioinformatics Conference (APBC 2007), 165-174, 2007.
70. M. Hayashida, T. Akutsu and H. Nagamochi, A novel clustering method for analysis of biological networks using maximal components of graphs,
    Proc. 5th Asia-Pacific Bioinformatics Conference (APBC 2007), 257-266, 2007.
71. T. Akutsu, D. Fukagawa and A. Takasu, Approximating tree edit distance through string edit distance,
    Proc. 17th International Symposium on Algorithms and Computation (ISAAC 2006), Lecture Notes in Computer Science 4288, 90-99, 2006.
72. J.B. Brown, Dukka Bahadur K. C., E. Tomita and T. Akutsu, Multiple methods for protein side chain packing using maximum weight cliques,
    Genome Informatics, 17-1 (The 6th Int. Workshop on Bioinformatics and Systems Biology), 3-12, 2006.
73. T. Akutsu and J.C. Nacher, Theoretical and computational analyses of structures of metabolic networks and protein-protein interaction networks (invited non-refereed paper),
    The First International Conference on Computational Systems Biology, 2006.
74. T. Akutsu, M. Hayashida, W-K. Ching and M.K. Ng, On the complexity of finding control strategies for boolean networks,
    Proc. 4th Asia-Pacific Bioinformatics Conference (APBC 2006), 99-108, 2006.
75. T. Akutsu and D. Fukagawa, On inference of a chemical structure from path frequency,
    Proc. 2005 International Joint Conference of InCoB, AASBi and KSBI (BIOINFO2005), 96-100, 2005.
76. Dukka Bahadur K.C., J.B. Brown, E. Tomita, J. Suzuki and T. Akutsu, Large scale protein side-chain packing based on maximum edge-weight clique finding algorithm,
    Proc. 2005 International Joint Conference of InCoB, AASBi and KSBI (BIOINFO2005), 228-233, 2005.
77. L.M.C. Meireles and T. Akutsu, A gibbs sampling approach to detection of tree motifs,
    Genome Informatics, 16-1 (The 5th Int. Workshop on Bioinformatics and Systems Biology), 34-43, 2005.
78. H.A. Moesa, Dukka Bahadur K.C. and Tatsuya Akutsu, Efficient determination of cluster boundaries for analysis of gene expression profile data using hierarchical clustering and wavelet transform,
    Genome Informatics, 16-1 (The 5th Int. Workshop on Bioinformatics and Systems Biology), 132-141, 2005.
79. S-Q. Zhang, M.K. Ng, W-K. Ching and T. Akutsu, A linear control model for gene intervention in a genetic regulatory network,
    Proc. IEEE International Conference on Granular Computing (GrC 2005), 354-358, 2005.
80. T. Akutsu and D. Fukagawa, Inferring a graph from path frequency,
    Proc. 16th Annual Symposium on Combinatorial Pattern Matching (CPM 2005), Lecture Notes in Computer Science 3537, 371-382, 2005.
81. T. Akutsu, Computational and statistical methods in bioinformatics (non-refereed tutorial paper),
    Post-Proceedings AM-2003, Lecture Notes in Computer Science 3430, 11-33, 2005.
82. Dukka Bahadur K.C., E. Tomita, J. Suzuki, K. Horimoto and T. Akutsu, Clique based algorithms for protein threading with profiles and constraints,
    Proc. 3rd Asia-Pacific Bioinformatics Conference (APBC 2005), 51-64, 2005.
83. D. Fukagawa and T. Akutsu, Fast algorithms for comparison of similar unordered trees,
    Proc. 15th Int Symp. Algorithms and Computation (ISAAC 2004), Lecture Notes in Computer Science 3341, 452-463, 2004.
84. T. Akutsu, Algorithms for point set matching with k-differences,
    Proc. 10th Int. Computing and Combinatorics Conference (COCOON 2004), Lecture Notes in Computer Science 3106, 249-258, 2004.
85. P. Mahe, N. Ueda, T. Akutsu, J-L. Perret and J-P. Vert, Extensions of marginalized graph kernels,
    Proc. 21st Int. Conf. Machine Learning (ICML 2004), 552-559, 2004.
86. M. Hayashida, N. Ueda and T. Akutsu, A simple method for inferring strengths of protein-protein interactions,
    Genome Informatics, 15-1 (The 4th Int. Workshop on Bioinformatics and Systems Biology), 56-68, 2004.
87. M. Itoh, T. Akutsu and M. Kanehisa, Clustering of database sequences for fast homology search using upper bounds on alignment score,
    Genome Informatics, 15-1 (The 4th Int. Workshop on Bioinformatics and Systems Biology), 93-104, 2004.
88. T. Akutsu, M. Hayashida, E. Tomita, J. Suzuki and K. Horimoto, Protein threading with profiles and constraints,
    Proc. IEEE 4th Symp. Bioinformatics and Bioengineering (BIBE 2004), 537-544, 2004.
89. Dukka Bahadur K.C., T. Akutsu, E. Tomita and T. Seki, Protein side-chain packing problem: a maximum edge-weight clique algorithmic approach,
    Proc. 2nd Asia-Pacific Bioinformatics Conference (APBC 2004), 191-200, 2004.
90. K. F. Aoki, A. Yamaguchi, Y. Okuno, T. Akutsu, N. Ueda, M. Kanehisa and H. Mamitsuka, Efficient tree-matching methods for accurate carbohydrate database queries,
    Genome Informatics 2003, 134-143, 2003.
91. D. Fukagawa and T. Akutsu, Performance analysis of a greedy algorithm for inferring boolean functions,
    Proc. 6th Int. Conf. Discovery Science (DS 2003), Lecture Notes in Artificial Intelligence 2843, 114-127, 2003.
92. T. Akutsu, Efficient extraction of mapping rules of atoms from enzymatic reaction data,
    Proc. 7th Int. Conf. Computational Molecular Biology (RECOMB 2003), 1-8, 2003.
93. K. C. D. Bahadur, T. Akutsu, E. Tomita, T. Seki and A. Fujiyama, Point matching under non-uniform distortions and protein side chain packing based on an efficient maximum clique algorithm,
    Genome Informatics, 13, 143-152, 2002.
94. T. Akutsu and S. Ott, Inferring a union of halfspaces from examples,
    Proc. 8th International Conference on Computing and Combinatorics (COCOON 2002), Lecture Notes in Computer Science 2387, 117-126, 2002.
95. T. Akutsu, H. Bannai, S. Miyano and S. Ott, On the complexity of deriving position specific score matrices from examples,
    Proc. 13th Annual Symposium on Combinatorial Pattern Matching (CPM 2002), Lecture Notes in Computer Science 2373, 168-177, 2002.
96. T. Akutsu and K. Horimoto, Local multiple alignment of numerical sequences: detection of subtle motifs from protein sequences and structures,
    Genome Informatics, 12, 83-92, 2001.
97. T. Akutsu, A local search algorithm for local multiple alignment: special case analysis and application to cancer classification,
    Proc. 2001 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 2001), 1284-1290, 2001.
98. T. Akutsu and S. Miyano, Selecting informative genes for cancer classification using gene expression data,
    2001 IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP), in CD-ROM BOOK, 2001.
99. T. Akutsu, S. Miyano and S. Kuhara, A simple greedy algorithm for finding functional relations: efficient implementation and average case analysis,
    Proc. The Third International Conference on Discovery Science (DS 2000), Lecture Notes in Artificial Intelligence 1967, 86-98, 2000.
100. T. Akutsu, H. Arimura and S. Shimozono, On approximation algorithms for local multiple alignment,
     Proc. 4th Int. Conf. Computational Molecular Biology (RECOMB 2000), 1-7, 2000.
101. T Akutsu, S. Miyano and S. Kuhara, Algorithms for identifying Boolean networks and related biological networks based on matrix multiplication and fingerprint function,
     Proc. 4th Int. Conf. Computational Molecular Biology (RECOMB 2000), 8-14, 2000.
102. T Akutsu, S. Miyano and S. Kuhara, Algorithms for inferring qualitative models of biological networks, Proc. Pacific Symposium on Biocomputing 2000 (PSB 2000), 290-301, 2000.
103. T.Akutsu and K.L.Sim, Protein threading based on multiple protein structure alignment,
     Genome Informatics 1999 (GIW '99), 23-29, 1999.
104. T. Akutsu, K. Kanaya, A. Ohyama and A. Fujiyama, Matching of spots in 2D electrophoresis images. Point matching under non-uniform distortions,
     Proc. 10th Annual Symposium on Combinatorial Pattern Matching (CPM '99), Lecture Notes in Computer Science 1645, 212-222, 1999.
105. T. Akutsu, S. Miyano and S. Kuhara, Identification of genetic networks from a small number of gene expression patterns under the boolean network model,
     Proc. Pacific Symposium on Biocomputing '99 (PSB '99), 17-28, 1999.
106. T. Akutsu, S. Kuhara, O. Maruyama and S. Miyano, A system for identifying genetic networks from gene expression patterns produced by gene disruptions and overexpressions,
     Genome Informatics 1998 (GIW '98), 151-160, 1998.
107. T. Akutsu, Approximation and exact algorithms for RNA secondary structure prediction and recognition of stochastic context-free languages,
     Proc. 9th Annual International Symposium on Algorithms and Computation (ISAAC '98), Lecture Notes in Computer Science 1533, 337-346, 1998.
108. T. Akutsu and M. Yagiura, On the complexity of deriving score functions from examples for problems in molecular biology,
     Proc. 25th Int. Colloquium on Automata, Languages, and Programming (ICALP '98), Lecture Notes in Computer Science 1443, 832-843, 1998.
109. T. Akutsu, S. Kuhara, O. Maruyama and S. Miyano, Identification of gene regulatory networks by strategic gene disruptions and gene overexpressions,
     Proc. 9th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA '98), 695-702, 1998.
110. T. Akutsu and H. Tashimo, Linear programming based approach to the derivation of a contact potential for protein threading,
     Proc. Pacific Symposium on Biocomputing '98 (PSB '98), 413-424, 1998.
111. T. Akutsu and S. Miyano, On the approximation of protein threading,
     Proc. First Annual International Conference on Computational Molecular Biology (RECOMB '97), 3-8, 1997.
     
112. T. Akutsu, H. Tamaki and T. Tokuyama, Distribution of distances and triangles in a point set and algorithms for computing the largest common point set,
     Proc. the 13th ACM Symposium on Computational Geometry, 314-323, 1997.
113. T. Akutsu and F. Bao, Approximating minimum keys and optimal substructure screens,
     Proc. 2nd International Conference on Computing and Combinatorics (COCOON '96) (LNCS 1090), 290-299, 1996.
114. T. Akutsu and H. Tashimo, Protein structure comparison using representation by line segment sequences,
     Proc. Pacific Symposium on Biocomputing '96 (PSB '96), 25-40, 1996.
115. T. Akutsu, K. Onizuka and M. Ishikawa, New hashing techniques and their application to a protein structure database system,
     Proc. 28th Hawaii Int. Conf. System Sciences (HICSS-28) 5, 197-206, 1995.
116. T. Akutsu, On determining the congruity of point sets in higher dimensions,
     Proc. 5th International Symposium on Algorithms and Computation (ISAAC '94) (LNCS 834), 38-46, 1994.
117. T. Akutsu and M. M. Halldorson, On the approximation of largest common subtrees and largest common point sets,
     Proc. 5th Annual International Symposium on Algorithms and Computation (ISAAC '94)(LNCS 834), 405-413, 1994.
118. T. Akutsu, Approximate string matching with don't care characters,
     Proc. 5th International Symposium on Combinatorial Pattern Matching (CPM '94)(LNCS 807), 240-249, 1994.
119. T. Akutsu, Efficient and robust three-dimensional pattern matching algorithms using hashing and dynamic programming techniques,
     Proc. 27th Hawaii Int. Conf. System Sciences (HICSS-27), 225-234, 1994.
120. T. Akutsu, A linear time pattern matching algorithm between a string and a tree,
     Proc. 4th International Symposium on Combinatorial Pattern Matching (CPM '93)(LNCS 684), 1-10, 1993.
121. T. Akutsu and A. Takasu, Inferring approximate functional dependencies from example data,
     Proc. AAAI-93 Workshop on Knowledge Discovery in Databases, 138-152, 1993.
122. T. Akutsu and A. Takasu, On PAC learnability of functional dependencies,
     Proc. Workshop on Algorithmic Learning Theory (ALT '92), 229-239, 1992.
123. T. Akutsu, Algorithms for determining the geometrical congruity in two and three dimensions, Proc. 3rd International Symposium on Algorithms and Computation (ISAAC '92)(LNCS 834), 279-288, 1992.
124. T. Akutsu, On the number of hands required to disassemble a composite object,
     Proc. 2nd International Symposium on Measurement and Control in Robotics (ISMCR '92), 63-68, 1992.
125. T. Akutsu, S. Yaoi, K. Sato and S. Enomoto, Development and comparison of search algorithms for robot motion planning in the configuration space,
     IEEE/RSJ International Workshop on Intelligent Robotics and Systems (IROS '91), 429-434, 1991.
126. T. Akutsu, Y. Aoki, S. Hasegawa, H. Imai and T. Tokuyama, The sum of smaller endpoint degree over edges of graphs and its applications to geometric problems,
     Proc. 3rd Canadian Conference on Computational Geometry, 145-148, 1991.
127. T. Akutsu and S. Ohsuga, CHEMILOG - a logic programming language/system for chemical information processing,
     Proc. International Conference on Fifth Generation Computer Systems 1998 (FGCS '88), 1176-1183, 1988.

Book Chapters

1. F. Wang, T. Akutsu, and T. Mori. Metrics for RNA secondary structure comparison,
   Methods in Molecular Biology, 2586, 79-88, 2023.
2. Z. Chen, X. Liu, F. Li, C. Li, T. Marquez-Lago, A. Leier, G. I. Webb, D. Xu, T. Akutsu, and J. Song, Systematic characterization of lysine post-translational modification sites ising MUscADEL,
   Methods in Molecular Biology, 2499, 205-219, 2022.
3. N. Nakajima, T. Akutsu, and R. Nakato, Databases for protein-protein interactions,
   Methods in Molecular Biology, 2361, 229-248, 2021.
4. T. Akutsu, Analysis of Boolean networks and Boolean models of metabolic networks,
   Application of Omics, AI and Blockchain in Bioinformatics Research (J. J-P. Tsai and K-L. Ng, eds.), World Scientific, 2019.
5. J. C. Nacher and T. Akutsu, Controllability methods for identifying associations between critical control ncRNAs and human diseases,
   Methods in Molecular Biology, 1912, 289-300, 2019.
6. T. Tamura and T. Akutsu, Theory and method of completion for a Boolean regulatory network using observed data,
   Biological Data Mining and Its Applications in Healthcare, Science, Engineering, and Biology Informatics: Volume 8 (X-L Li, S-K Ng and J. T-L. Wang, eds.), World Scientific, 123-146, 2014.
7. E. Tomita, T. Akutsu and T. Matsunaga, Efficient algorithms for finding maximum and maximal cliques: Effective tools for bioinformatics,
   Biomedical Engineering, Trends in Electronics, Communications and Software (A. N. Laskovski, ed.), InTech, 625-640, 2011.
8. T. Akutsu, Sequence alignment algorithms: Applications to glycans and trees and tree-like structures,
   Handbook of Chemoinformatics Algorithms (J-L Faulon and A. Bender, eds.), CRC Press, 363-381, 2010.
9. T. Akutsu and W-K. Ching, Analysis and control of deterministic and probabilistic Boolean networks,
   Elements of Computational Systems (H. M. Lodhi and S. H. Muggleton, eds.), Wiley, 235-255, 2010.
10. T. Akutsu and M. Hayashida, Domain-based prediction and analysis of protein-protein interactions,
    Biological Data Mining in Protein Interaction Networks (X-L Li and S-K Ng, eds.), IGI Global, 29-44, 2009.
11. T. Akutsu, Algorithmic aspects of protein threading,
    Advanced Data Mining Technologies in Bioinformatics (H-H Hsu, ed.), Idea Group Pub., 118-135, 2006.
12. J-P. Vert, H. Saigo and T. Akutsu, Local alignment kernels for biological sequences,
    Kernel Methods in Computational Biology (B. Schoelkopf, K. Tsuda and J-P. Vert, eds.) The MIT Press, 131-153, 2004.
13. T. Akutsu and S. Miyano, Selecting informative genes for cancer classification using gene expression data,
    Computational and Statistical Approaches to Genomics (W. Zhang and I. Shmulevich, eds.), Kluwer Academic Pub., 79-91, 2002.