Publications

Efficient Semi-Supervised Learning on Locally Informative Multiple Graphs: Shiga, M. and Mamitsuka, H.; Pattern Recognition, 45(3), 1035-1049, 2012.; [DOI].
Genome-wide Integration on Transcription Factors, Histone Acetylation and Gene Expression Reveals Genes Co-regulated by Histone Modification Patterns.: Natsume-Kitatani, Y., Shiga, M. and Mamitsuka, H.; PLoS One, 6(7), e22281, 2011.; [DOI].
Discriminative Graph Embedding for Label Propagation.: Nguyen, C. H. and Mamitsuka, H.; IEEE Transactions on Neural Networks, 22(9), 1395-1495, 2011.; [DOI].
Calpain Cleavage Prediction Using Multiple Kernel Learning.: duVerle, D., Ono, Y., Sorimachi, H. and Mamitsuka, H.; PLoS One, 6(5), e19035, 2011.; [DOI].
ROS-DET: Robust Detector of Switching Mechanisms in Gene Expression.: Kayano, M., Takigawa, I., Shiga, M., Tsuda, K. and Mamitsuka, H.; Nucleic Acids Research, 39 (11), e74, 2011.; [DOI].
Mining Significant Substructure Pairs for Interpreting Polypharmacology in Drug-Target Network.: Takigawa, I., Tsuda, K. and Mamitsuka, H.; PLoS One, 6(2), e16999, 2011.; [DOI].
Efficiently Mining δ-Tolerance Closed Frequent Subgraphs.: Takigawa, I. and Mamitsuka, H.; Machine Learning, 82(2), 95-121, 2011.; [DOI].
Ensemble Approaches for Improving HLA Class I-peptide Binding Prediction.: Hu, X., Mamitsuka, H. and Zhu, S.; Journal of Immunological Methods, 374(1/2), 47-52, 2011.; [DOI].
A Spectral Approach to Clustering Numerical Vectors as Nodes in a Network.: Shiga, M., Takigawa, I. and Mamitsuka, H.; Pattern Recognition, 44(2), 236-251, 2011.; [DOI].
Mining Metabolic Pathways through Gene Expression.: Hancock, T., Takigawa, I. and Mamitsuka, H.; Bioinformatics, 26(17), 2128-2135, 2010.; [DOI].
MetaMHC: A Meta Approach to Predict Peptides Binding to MHC Molecules: Hu, X., Zhou, W., Udaka, K., Mamitsuka, H. and Zhu, S.; Nucleic Acids Research, 38, W474-W479, 2010.; [DOI].
A Markov Classification Model for Metabolic Pathways.: Hancock, T. and Mamitsuka, H.; Algorithms for Molecular Biology, 5 (1), 10, 2010.; Special Issue: selected papers from WABI 2009; [DOI].
On Network-based Kernel Methods for Protein-Protein Interactions with Applications in Protein Functions.: Li, L., Ching, W-K., Chan, Y-M. and Mamitsuka, H.; Journal of Systems Science and Complexity 23(5), 917-930, 2010.; [DOI].
TAP Hunter: A SVM-based System for Predicting TAP Ligands using Local Description of Amino Acid Sequence.: Lam, T. H., Mamitsuka, H., Ren, E. C., and Tong, J. C.; Immunome Research, 6 (Suppl. 1), S6, 2010.; [DOI].
Efficiently Finding Genome-wide Three-way Gene Interactions from Transcript- and Genotype-Data.: Kayano, M., Takigawa, I., Shiga, M., Tsuda, K. and Mamitsuka, H.; Bioinformatics, 25 (21), 2735-2743, 2009.; [DOI].
Enhancing MEDLINE Document Clustering by Incorporating MeSH Semantic Similarity.: Zhu, S., Zeng, J. and Mamitsuka, H.; Bioinformatics, 25 (15), 1944-1951, 2009.; [DOI].
Field Independent Probabilistic Model for Clustering Multi-Field Documents.: Zhu, S., Takigawa, I., Zeng, J. and Mamitsuka, H.; Information Processing and Management, 45 (5), 555-570, 2009.; [DOI, PDF (Advance access)].
HAMSTER: Visualizing Microarray Experiments as a Set of Minimum Spanning Trees.: Wan, R., Kiseleva, L., Harada, H., Mamitsuka, H. and Horton, P.; Source Code for Biology and Medicine, 4, 8, 2009.; [DOI].
Mining Significant Tree Patterns in Carbohydrate Sugar Chains.: Hashimoto, K., Takigawa, I., Shiga, M., Kanehisa, M. and Mamitsuka, H.; Proceedings of the Seventh European Conference on Computational Biology (ECCB 2008), (Bioinformatics, 24 (16), 2008), i167-i173, Cagliari, Sardinia-Italy, September, 2008, Oxford University Press.; [DOI].
A New Efficient Probabilistic Model for Mining Labeled Ordered Trees Applied to Glycobiology.: Hashimoto, K., Aoki-Kinoshita, K. F., Ueda, N., Kanehisa, M. and Mamitsuka, H.; ACM Transactions on Knowledge Discovery from Data, 2 (1), Article No. 6, 2008.; [DOI].
Probabilistic Path Ranking Based on Adjacent Pairwise Coexpression for Metabolic Transcripts Analysis.: Takigawa, I. and Mamitsuka, H.; Bioinformatics, 24 (2), 250-257, 2008.; [DOI].
Annotating Gene Function by Combining Expression Data with a Modular Gene Network.: Shiga, M., Takigawa, I. and Mamitsuka, H.; Proceedings of the Fifteenth International Conference on Intelligent Systems for Molecular Biology (ISMB/ECCB 2007), (Bioinformatics, 23 (13), 2007), i468-i478, Vienna, Austria, July, 2007, Oxford University Press.; [DOI].
A Hidden Markov Model-based Approach for Identifying Timing Differences in Gene Expression under Different Experimental Factors.: Yoneya, T. and Mamitsuka, H.; Bioinformatics, 23 (7), 842-849, 2007.; [DOI].
Identification of Endocrine Disruptor Biodegradation by Integration of Structure-activity Relationship with Pathway Analysis.: Kadowaki, T., Wheelock, C. E., Adachi, T., Kudo, T., Okamoto, S., Tanaka, N., Tonomura, K., Tsujimoto, G., Mamitsuka, H., Goto, S. and Kanehisa, M.; Environmental Science & Technology, 41 (23), 7997-8003, 2007.; [DOI].
Selecting Features in Microarray Classification Using ROC Curves: Mamitsuka, H.; Pattern Recognition, 39 (12), 2393-2404, 2006.; [DOI].
ProfilePSTMM: Capturing Tree-structure Motifs in Carbohydrate Sugar Chains.: Aoki, K. F., Ueda, N., Mamitsuka, H. and Kanehisa, M.; Proceedings of the Fourteenth International Conference on Intelligent Systems for Molecular Biology (ISMB 2006), (Bioinformatics, 22 (14), e25-e34), Fortaleza, Brazil, August, 2006, Oxford University Press.; [DOI].
Improving MHC Binding Peptide Prediction by Incorporating Binding Data of Auxiliary MHC Molecules.: Zhu, S., Udaka, K., Sidney, J., Sette, A., Aoki-Kinoshita, K. F. and Mamitsuka, H.; Bioinformatics, 22 (13), 1648-1655, 2006.; [DOI].
Query-Learning-Based Iterative Feature-Subset Selection for Learning from High-Dimensional Data Sets.: Mamitsuka, H.; Knowledge and Information Systems, 9 (1), 91-108, 2006.; [DOI].
Application of a New Probabilistic Model for Mining Implicit Associated Cancer Genes from OMIM and Medline.: Zhu, S., Okuno, Y., Tsujimoto, G. and Mamitsuka, H.; Cancer Informatics, 2, 361-371, 2006.; [PubMed, Cancer Informatics, Bibtex].
A Probabilistic Model for Mining Implicit ``Chemical Compound - Gene'' Relations from Literature.: Zhu, S., Okuno, Y., Tsujimoto, G. and Mamitsuka, H.; Proceedings of the Fourth European Conference on Computational Biology (ECCB/JBI 2005), (Bioinformatics, 21, Supplement 2, ii245-ii251), Madrid, Spain, September, 2005, Oxford University Press.; [DOI].
Finding the Biologically Optimal Alignment of Multiple Sequences: Mamitsuka, H.; Artificial Intelligence in Medicine, 35 (1), 9-18, 2005.; [DOI].
Probabilistic Model for Mining Labeled Ordered Trees: Capturing Patterns in Carbohydrate Sugar Chains.: Ueda, N., Aoki-Kinoshita, K. F., Yamaguchi, A., Akutsu, T. and Mamitsuka, H.; IEEE Transactions on Knowledge and Data Engineering, 17 (8), 1051-1064, 2005.; [DOI].
A Score Matrix to Reveal the Hidden Links in Glycans.: Aoki, K. F., Mamitsuka, H., Akutsu, T. and Kanehisa, M.; Bioinformatics, 21 (8), 1457-1463, 2005.; [DOI].
Mining New Protein-Protein Interactions - Using a Hierarchical Latent-variable Model to Determine the Function of a Functionally Unknown Protein.: Mamitsuka, H.; IEEE Engineering in Medicine and Biology Magazine, 24 (3), 103-108, 2005.; [DOI].
Essential Latent Knowledge for Protein-Protein Interactions: Analysis by an Unsupervised Learning Approach.: Mamitsuka, H.; IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2 (2), 119-130, 2005.; [DOI].
Efficient Unsupervised Mining from Noisy Co-occurrence Data.: Mamitsuka, H.; New Mathematics and Natural Computation, 1 (1), 173-193, 2005.; [DOI].
The Evolutionary Repertoires of the Eukaryotic-type ABC Transporters in terms of the Phylogeny of ATP-binding Domains in Eukaryotes and Prokaryotes.: Igarashi, Y., Aoki, K. F., Mamitsuka, H., Kuma, K. and Kanehisa, M.; Molecular Biology and Evolution, 21 (11), 2149-2160, 2004.; [DOI].
Finding the Maximum Common Subgraph of a Partial k-Tree and a Graph with a Polynomially Bounded Number of Spanning Trees.: Yamaguchi, A., Aoki, K. F. and Mamitsuka, H.; Information Processing Letters, 92 (2), 57-63, 2004.; [DOI].
Application of a New Probabilistic Model for Recognizing Complex Patterns in Glycans.: Aoki, K. F., Ueda, N., Yamaguchi, A., Kanehisa, M., Akutsu, T. and Mamitsuka, H.; Proceedings of the Twelfth International Conference on Intelligent Systems for Molecular Biology (ISMB/ECCB 2004), (Bioinformatics, 20, Supplement 1, i6-i14), Glasgow, Scotland, August, 2004, Oxford University Press.; [DOI].
KCaM (KEGG Carbohydrate Matcher): A Software Tool for Analyzing the Structures of Carbohydrate Sugar Chains.: Aoki, K. F., Yamaguchi, A., Ueda, N., Akutsu, T., Mamitsuka, H., Goto, S. and Kanehisa, M.; Nucleic Acids Research, 32, W267-W272, 2004.; [DOI].
Managing and Analyzing Carbohydrate Data.: Aoki, K. F., Ueda, N., Yamaguchi, A., Akutsu, T., Kanehisa, M. and Mamitsuka, H.; ACM SIGMOD Record, 33 (2), 33-38, 2004.; [DOI, PDF].
Mining Biologically Active Patterns in Metabolic Pathways using Microarray Expression Profiles.: Mamitsuka, H., Okuno, Y. and Yamaguchi, A.; ACM SIGKDD Explorations, 5 (2):113-121, 2003.; [DOI, PDF].
Empirical Evaluation of a Dynamic Experiment Design Method for Prediction of MHC Class I-binding Peptides.: Udaka, K., Mamitsuka, H., Nakaseko, Y. and Abe, N.; Journal of Immunology, 169 (10):5744-5753, 2002.; [PubMed, PDF, Bibtex].
Prediction of MHC Class I Binding Peptides by a Query Learning Algorithm based on Hidden Markov Models.: Udaka, K., Mamitsuka, H., Nakaseko, Y. and Abe, N.; Journal of Biological Physics, 28 (2):183-194, 2002.; [DOI].
Predicting Peptides that Bind to MHC Molecules using Supervised Learning of Hidden Markov Models.: Mamitsuka, H.; Proteins: Structure, Function, and Genetics, 33 (4):460-471, 1998.; [DOI]
Predicting Protein Secondary Structures using Stochastic Tree Grammars.: Abe, N. and Mamitsuka, H.; Machine Learning, 29 (2-3):275-301, 1997.; [DOI]
A Learning Method of Hidden Markov Models for Sequence Discrimination.: Mamitsuka, H.; Journal of Computational Biology, 3 (3):361-373, 1996.; [DOI]
Representing Inter-residue Dependencies in Protein Sequences with Probabilistic Networks.: Mamitsuka, H.; Computer Applications in the Biosciences, 11 (4):413-422, 1995.; [DOI]
Alpha-helix Region Prediction with Stochastic-rule Learning.: Mamitsuka, H. and Yamanishi, K.; Computer Applications in the Biosciences, 11 (4):399-411, 1995.; [DOI]

Kernels for Link Prediction with Latent Feature Models.: Nguyen, C. H. and Mamitsuka, H.; Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2011), Part II (Lecture Notes in Artificial Intelligence, 6912), 517-532, Athens, Greece, September, 2011, Springer.; [DOI].
Boosted Optimization for Network Classification.: Hancock, T. and Mamitsuka, H.,; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010) (JMLR: Workshop and Conference Proceedings, Vol. 9) , 305-312, Sardinia, Italy, May 2010, MIT Press.; [PDF (MIT Press), Bibtex].
A Spectral Clustering Approach to Optimally Combining Numerical Vectors with a Modular Network.: Shiga, M., Takigawa, I. and Mamitsuka, H.,; Proceedings of the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2007), 647-656, San Jose, CA, USA, August 2007, ACM Press.; [DOI, PDF (Preprints)].
A Probabilistic Model for Clustering Text Documents with Multiple Fields.: Zhu, S., Takigawa, I., Zhang, S. and Mamitsuka, H.; Proceedings of the 29th European Conference on Information Retrieval (ECIR 2007, Lecture Notes in Computer Science, 4425), 331-342, Roma, Italy, April, 2007, Springer-Verlag.; [DOI].
A New Efficient Probabilistic Model for Mining Labeled Ordered Trees.: Hashimoto, K., Aoki-Kinoshita, K. F., Ueda, N., Kanehisa, M. and Mamitsuka, H.,; Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006), 177-186, Philadelphia, PA, USA, August 2006, ACM Press.; [DOI, PDF (Preprints), Bibtex].
A Hierarchical Mixture of Markov Models for Finding Biologically Active Metabolic Paths using Gene Expression and Protein Classes.: Mamitsuka, H. and Okuno, Y.; Proceedings of the IEEE Computational Systems Bioinformatics Conference (CSB 2004), 341-352, Stanford, CA, USA, August, 2004, IEEE Computer Society Press.; [DOI, PDF, Bibtex].
A General Probabilistic Framework for Mining Labeled Ordered Trees.: Ueda, N., Aoki, K. F. and Mamitsuka, H.; Proceedings of the Fourth SIAM International Conference on Data Mining (SDM 2004), 357-368, Orlando, FL, USA, April, 2004, SIAM.; [PDF, Bibtex].
Efficient Tree-Matching Methods for Accurate Carbohydrate Database Queries.: Aoki, K. F., Yamaguchi, A., Okuno, Y., Akutsu, T., Ueda, N., Kanehisa, M. and Mamitsuka, H.; Proceedings of the Fourteenth International Conference on Genome Informatics (GIW 2003, Genome Informatics, 14), 134-143, Yokohama, Japan, December, 2003, Universal Academy Press.; [PubMed, Bibtex].
Hierarchical Latent Knowledge Analysis for Co-occurrence Data.: Mamitsuka, H.; Proceedings of the Twentieth International Conference on Machine Learning (ICML 2003), 504-511, Washington DC, USA, August, 2003, AAAI Press.; [Abstract (ICML2003), Bibtex].
Efficient Unsupervised Mining from Noisy Data Sets: Application to Clustering Co-occurrence Data.: Mamitsuka, H.; Proceedings of the Third SIAM International Conference on Data Mining (SDM 2003), 239-243, San Francisco, CA, USA, May, 2003, SIAM.; [PDF].
Iteratively Selecting Feature Subsets for Mining from High-Dimensional Databases.: Mamitsuka, H.; Proceedings of the Sixth European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2002, Lecture Notes in Artificial Intelligence, 2431), 361-372, Helsinki, Finland, August, 2002, Springer-Verlag.; [DOI].
Efficient Mining from Large Databases by Query Learning.: Mamitsuka, H. and Abe, N.; Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), 575-582, Stanford Univ., CA, USA, June, 2000, Morgan Kaufmann.; [PDF (Preprints), Bibtex].
Query Learning Strategies using Boosting and Bagging.: Abe, N. and Mamitsuka, H.; Proceedings of the Fifteenth International Conference on Machine Learning (ICML 98), 1-9, Madison, WI, USA, July, 1998, Morgan Kaufmann.; [PDF (Preprints), Bibtex].
Learning Personal Preferences by On-line Prediction Algorithms.: Nakamura, A., Abe, N., Mamitsuka, H. and Toba, H.; Poster Session Abstracts of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI 97), 75, Nagoya, Japan, August, 1997.
Supervised Learning of Hidden Markov Models for Sequence Discrimination.: Mamitsuka, H.; Proceedings of the First International Conference on Computational Molecular Biology (RECOMB 97), 202-208, Santa Fe, NM, USA, January, 1997, ACM Press.; [ACM, Bibtex].
Predicting Location and Structure of Beta-sheet Regions using Stochastic Tree Grammars.: Mamitsuka, H. and Abe, N.; Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology (ISMB 94), 276-284, Stanford Univ., CA, USA, August, 1994, AAAI Press.; [PubMed, PDF (Preprints), Bibtex].
A New Method for Predicting Protein Secondary Structures based on stochastic tree grammars.: Abe, N. and Mamitsuka, H.; Proceedings of the Eleventh International Conference on Machine Learning (ICML 94), 3-11, New Brunswick, NJ, USA, July, 1994, Morgan Kaufmann.; [Bibtex].
Protein Alpha-helix Region Prediction based on Stochastic Rule Learning.: Mamitsuka, H. and Yamanishi, K.; Proceedings of the 26th Annual Hawaii International Conference on System Sciences (HICSS 26), 659-668, Maui, HI, USA, January, 1993, IEEE Computer Society Press.; [IEEE Xplore, Bibtex].

Algorithms for Finding a Minimum Repetition Representation of a String: Nakamura, A., Saito, T., Takigawa, I., Mamitsuka, H. and Kudo, M.; Proceedings of the Seventeenth Symposium on String Processing and Information Retrieval (SPIRE 2010, Lecture Notes in Computer Science, 6393), 185-190, Los Cabos, Mexico, October, 2010.; [DOI].
Efficient Probabilistic Latent Semantic Analysis through Parallelization.: Wan, R., Vo, N. A. and Mamitsuka, H.; Proceedings of the Fifth Asia Information Retrieval Symposium (AIRS2009, Lecture Notes in Computer Science, 5839), 432-443, Sapporo, Japan, October, 2009, Springer-Verlag.; [DOI].
A Study of Network-based Kernel Methods on Protein-Protein Interaction for Protein Functions Prediction.: Ching, W-K., Li, L., Chan, Y-M. and Mamitsuka, H.; Proceedings of the Third International Symposium on Optimization and Systems Biology (OSB 2009, Lecture Notes in Operations Research, 11), 25-32, Zhangjiajie, China, September, 2009, APORC Press.; [PDF (APORC), Bibtex].
Cleaning Microarray Expression Data Using Markov Random Fields based on Profile Similarity.: Wan, R., Mamitsuka, H. and Aoki, K. F.; Proceedings of the Twentieth ACM Symposium on Applied Computing (SAC 2005), 206-207, Santa Fe, NM, USA, March, 2005, ACM Press.; [DOI].
Finding the Maximum Common Subgraph of a Partial k-Tree and a Graph with a Polynomially Bounded Number of Spanning Trees.: Yamaguchi, A. and Mamitsuka, H.; Proceedings of the Fourteenth International Symposium on Algorithm and Computation (ISAAC 2003, Lecture Notes in Computer Science, 2906), 58-67, Kyoto, Japan, December, 2003, Springer-Verlag.; [DOI].
Selective Sampling with a Hierarchical Latent Variable Model.: Mamitsuka, H.; Proceedings of the Fifth International Symposium on Intelligent Data Analysis (IDA 2003, Lecture Notes in Computer Science, 2810), 352-363, Berlin, Germany, August, 2003, Springer-Verlag.; [DOI].
Detecting Experimental Noise in Protein-Protein Interactions with Iterative Sampling and Model-based Clustering.: Mamitsuka, H.; Proceedings of the Third IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2003), 385-392, Bethesda, MD, USA, March, 2003, IEEE Computer Society Press.; [DOI].
Empirical Evaluation of Ensemble Feature Subset Selection Methods for Learning from a High-Dimensional Database in Drug Design.: Mamitsuka, H.; Proceedings of the Third IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2003), 253-257, Bethesda, MD, USA, March, 2003, IEEE Computer Society Press.; [DOI].

Variational Bayes Learning over Multiple Graphs.: Shiga, M. and Mamitsuka, H.; Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010), 166-171, Kitilla, Finland, September, 2010.; [DOI].
A Markov Classification Model for Metabolic Pathways.: Hancock, T. and Mamitsuka, H.; Proceedings of the Ninth Workshop on Algorithms in Bioinformatics (WABI 2009), (Lecture Notes in Bioinformatics, 5724), 121-132 , Philadelphia, PA, USA, September, 2009, Springer-Verlag.; [DOI].
Applying Gaussian Distribution-dependent Criteria to Decision Trees for High-Dimensional Microarray Data: Wan, R., Takigawa, I. and Mamitsuka, H.; Proceedings of 2006 VLDB Workshop on Data Mining in Bioinformatics (Lecture Notes in Bioinformatics, 4316), 40-49 , Seoul, Korea, September, 2006, Springer-Verlag.; [DOI].
Comprehensive Analysis and Prediction of Synthetic Lethality Using Subcellular Locations.: Yamada, T., Kawashima, S., Mamitsuka, H., Goto, S. and Kanehisa, M.; Proceedings of the Fifth International Workshop on Bioinformatics and Systems Biology (IBSB 2005, Genome Informatics, 16 (1)), 150-158, Berlin, Germany, August, 2005, Universal Academy Press.; [PubMed, PDF, Bibtex].
Efficient Mining from Heterogeneous Data Sets for Predicting Protein-Protein Interactions.: Mamitsuka, H.; Proceedings of the Fourteenth International Workshop on Database and Expert Systems, 32-36, Prague, Czech Republic, September, 2003, IEEE Computer Society Press.; [DOI].
Prediction of Beta-sheet Structures using Stochastic-Tree Grammars.: Mamitsuka, H. and Abe, N.; Proceedings of Genome Informatics Workshop 1994, 19-28, Yokohama, Japan, December, 1994, Universal Academy Press.; [PDF (Preprints), Bibtex].
Representing Inter-residue Dependencies in Protein Sequences with Probabilistic Networks.: Mamitsuka, H.; Proceedings of Genome Informatics Workshop IV, 46-55, Yokohama, Japan, December, 1993, Universal Academy Press.; [PDF, Bibtex].
Protein Alpha-Helix Region Prediction Using Stochastic-Rule Learning. (in Japanese): Mamitsuka, H. and Yamanishi, K.; Proceedings of Genome Infomartics Workshop III, Yokohama, Japan, December, 1992, Universal Academy Press.; [Abstract (GenomeNet), Bibtex].
Protein Secondary Structure Prediction based on Stochastic Rule Learning.: Mamitsuka, H. and Yamanishi, K.; Proceedings of the Third Annual Workshop on Algorithmic Learning Theory (ALT92) (Lecture Notes in Artificial Intelligence, 743), 240-251, Tokyo, Japan, October, 1992, Springer-Verlag.; [SpringerLink, Bibtex].

Clustering Genes with Expression and Beyond.: Shiga, M. and Mamitsuka, H.; WIRES Data Mining and Knowledge Discovery, 1(6), 496-511, 2011.; Invited Review Paper.; [DOI].
Glycoinformatics: Data Mining-based Approaches.: Mamitsuka, H.; Chimia, 65(1/2), 10-13, 2011.; Invited Review Paper.; [DOI].
Discovering Network Motifs in Protein Interaction Networks.: Wan, R. and Mamitsuka, H.; Biological Data Mining in Protein Interaction Networks, Chapter 8, 117-143, 2009, IGI Global.; [Bibtex].
Informatic Innovations in Glycobiology: Relevance to Drug Discovery.: Mamitsuka, H.; Drug Discovery Today, 13 (3/4), 118-123, 2008.; Invited Review Paper.; [DOI].
Computational Intelligence in Solving Bioinformatics Problems: Cios, K. J., Mamitsuka, H., Nagashima, T. and Tadeusiewicz, R.; Artificial Intelligence in Medicine, 35 (1), 1-8, 2005.; [DOI].
Efficient Data Mining by Active Learning.: Mamitsuka, H. and Abe, N.; Progress in Discovery Science, Lecture Notes in Artificial Intelligence, 2281:258-267, 2002, Springer-Verlag.; [DOI].
Distributed and Active Learning.: Abe, N., Yamanishi, K., Nakamura, A., Mamitsuka, H., Takeuchi, J. and Li, H.; Foundations of Real World Intelligence, 189-250, 2001, CSLI Publications.; [Book excerpt, Bibtex].

Stochastic Knowledge Representations and Machine Learning Strategies for Biological Sequence Analysis.: Mamitsuka, H.; PhD (Dr. Sc.) Dissertation, University of Tokyo, 1999.; [PDF (Preprints, 1.2Mbytes), Bibtex].

Mining Patterns from Glycan Structures.: Takigawa, I., Hashimoto, K., Shiga, M., Kanehisa, M. and Mamitsuka, H.; Proceedings of the International Beilstein Symposium on Glyco-Bioinformatics, 13-24, 2010.; [Beilstein Institute, Bibtex].
On the Performance of Methods for Finding a Switching Mechanism in Gene Expression.: Kayano, M., Takigawa, I., Shiga, M., Tsuda, K. and Mamitsuka, H.; Proceedings of the Tenth Annual International Workshop on Bioinformatics and Systems Biology (IBSB 2010, Genome Informatics, 24), 69-83, Kyoto, Japan, July, 2010, Imperial College Press.; [DOI].
Active Pathway Identification and Classification with Probabilistic Ensembles.: Hancock, T. and Mamitsuka, H.; Proceedings of the Ninth Annual International Workshop on Bioinformatics and Systems Biology (IBSB 2009, Genome Informatics, 22), 30-40, Boston, USA, July, 2009, Imperial College Press.; [DOI].
Annotating Gene Functions with Integrative Spectral Clustering on Microarray Expressions and Sequences.: Li, L., Shiga, M., Ching, W.-K. and Mamitsuka, H.; Proceedings of the Ninth Annual International Workshop on Bioinformatics and Systems Biology (IBSB 2009, Genome Informatics, 22), 95-120, Boston, USA, July, 2009, Imperial College Press.; [DOI].
CaMPDB: a Resource for Calpain and Modulatory Proteolysis.: duVerle, D., Takigawa, I., Ono, Y., Sorimachi, H. and Mamitsuka, H.; Proceedings of the Ninth Annual International Workshop on Bioinformatics and Systems Biology (IBSB 2009, Genome Informatics, 22), 202-214, Boston, USA, July, 2009, Imperial College Press.; [DOI].
Semi-Supervised Graph Partitioning with Decision Trees.: Hancock, T. and Mamitsuka, H.; Proceedings of the Eighth Annual International Workshop on Bioinformatics and Systems Biology (IBSB 2008, Genome Informatics, 20), 102-111, Berlin, Germany, June, 2008, Imperial College Press.; [DOI].
A Framework for Determining Outlying Microarray Experiments.: Wan, R., Wheelock, A. and Mamitsuka, H.; Proceedings of the Eighth Annual International Workshop on Bioinformatics and Systems Biology (IBSB 2008, Genome Informatics, 20), 64-76, Berlin, Germany, June, 2008, Imperial College Press.; [DOI].
PURE: A PubMed Article Recommendation System Based on Content-based Filtering.: Yoneya, T. and Mamitsuka, H.; Proceedings of the Seventh International Workshop on Bioinformatics and Systems Biology (IBSB 2007, Genome Informatics, 18), 267-276, Tokyo, Japan, July, 2007, Imperial College Press.; [DOI].
Predicting Implicit Associated Cancer Genes from OMIM and MEDLINE by a New Probabilistic Model: Zhu, S., Okuno, Y., Tsujimoto, G. and Mamitsuka, H.; BMC Systems Biology, 1 (Suppl 1), P16, 2007.; [Full text, Bibtex].
Passage Retrieval with Vector Space and Query-Level Aspect Models: Wan, R., Ngoc, V. A., and Mamitsuka, H.; The Sixteenth Text REtrieval Conference (TREC 2007) Proceedings (NIST (National Institute of Standards and Technology) Special Publication: SP 500-274), 37, 2007.; [PDF, Bibtex].
Combining Vector-Space and Word-Based Aspect Models for Passage Retrieval,: Wan, R., Takigawa, I., Ngoc, V. A., and Mamitsuka, H.; The Fifteenth Text REtrieval Conference (TREC 2006) Proceedings (NIST (National Institute of Standards and Technology) Special Publication: SP 500-272), 45, 2007.; [PDF, Bibtex].
Empirical Comparison of Competing Query Learning Methods.: Abe, N., Mamitsuka, H. and Nakamura, A.; Proceedings of the First International Conference on Discovery Science (Lecture Notes in Artificial Intelligence, 1532), 387-388, Fukuoka, Japan, December, 1998, Springer-Verlag.; [SpringerLink, Bibtex].

Clustering Analysis for Combining Multiple Genomic Data.: Shiga, M., Takigawa, I. and Mamitsuka, H.; Seibutsu-butsuri (Biophysics), 48 (3), 190-194, 2008.; [DOI].
Active Ensemble Learning - Applications to Data Mining and Bioinformatics - . (in Japanese): Mamitsuka, H. and Abe, N.; IEICE Transactions, J85-DII (5):717-724, 2002.; Invited paper.; [Abstract (IEICE), Bibtex].

[ Bio-Knowledge Engineering | Bioinformatics Center | Institute for Chemical Research | Kyoto University ]

Last Update: April 1, 2010

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