アブストラクト
| Date | 2:00pm May 21, 2019 |
| Speaker |
Xiaoli Li, Head of Data Analytics Department and a principal scientist at Institute for Infocomm Research, A*STAR Singapore. He also holds adjunct position at School of Computer Science and Engineering, Nanyang Technological University. His research interests include data mining, machine learning, AI, and bioinformatics. He has served as (senior) PC member/workshop chair/session chair in leading data mining and AI related conferences, such as KDD, ICDM, SDM, PKDD/ECML, WWW, IJCAI, AAAI, ACL and CIKM. He has published more than 180 peer-reviewed papers, including top tier conferences, such as KDD, ICDM, SDM, PKDD/ECMLICDE, ICML, IJCAI, AAAI, ACL, SIGIR, EMNLP, CIKM, UbiCom etc, as well as some top tier journals such as IEEE Transactions TKDE, IEEE Transactions on Reliability, Bioinformatics, PLOS Computational Biology. Some of his representative research publications include: positive unlabelled based learning (more than 2000 citations), social/biological network mining (more than 1000 citations). He also received 4 Best Paper Awards from reputable international conferences and 2 Best Performance Awards from international benchmark competitions. With rich translational experience in working with industry, Dr Li has led over 10 R&D projects in collaboration with industry partners across sectors, including leading aerospace companies, banks, telecom companies, insurance companies etc. |
| Title | Network biomarker for disease diagnosis and dynamic network biomarker for disease prediction |
| Abstract | Computational prediction of drug–target interactions (DTIs) has become an essential task in the drug discovery process. It narrows down search space by suggesting potential interaction candidates for validation via wet-lab experiments that are well known to be expensive and time-consuming. The newly discovered DTIs are critical for discovering novel targets interacting with existing drugs, as well as new drugs targeting certain disease associated genes. In this talk, I will introduce a few recently proposed matrix factorization based computational techniques for DTI prediction. |