Institute of Information Science, Academia Sinica

This talk addresses data mining in bioinformatics by examining two research problems: structure matching and network inference. Structure matching finds many applications in clustering, classification and database searching while network inference helps biomedical researchers better understand gene interactions and disease progression in the cell.

In the first part of this talk, I will present a new method, named RKalign, for aligning and matching two known RNA secondary structures with pseudoknots. RKalign adopts the partition function methodology to calculate the posterior log-odds scores of the alignments between bases or base pairs of the two RNAs with a dynamic programming algorithm. The posterior log-odds scores are then used to calculate the expected accuracy of an alignment between the RNAs. The goal is to find an optimal alignment with the maximum expected accuracy. RKalign employs a greedy algorithm to achieve this goal. The performance of RKalign is investigated and compared with existing tools for RNA structure alignment. RKalign is implemented in Java and freely accessible on the Internet. As more and more pseudoknots are revealed, collected and stored in public databases, we anticipate that a tool like RKalign will play a significant role in data comparison, annotation, analysis, and retrieval in these databases. In the second part of this talk, I will present an overview of network inference methods, and then describe a semi-supervised learning approach to inferring gene regulatory networks from gene expression data.

Jason T. L. Wang received the B.S. degree in mathematics from National Taiwan University in 1980 and the Ph.D. degree in computer science from the Courant Institute of New York University in 1991. He is a professor of bioinformatics and computer science at the New Jersey Institute of Technology, New Jersey, USA. His research interests focus on data mining in bioinformatics. In this and related areas, he has published seven books and well over 100 peer-reviewed articles.