Managing and Mining Biological Networks
Dr. Jiong Yang
Electrical Engineering and Computer Science Department
Case Western University
3:45 pm, March 16, 2011, Room 228 MSB
A large amount of biological data can be represented as graphs, e.g., gene regulatory networks, protein interaction networks, etc. These graphs usually consist of tens of thousands vertices and edges. This poses not only a computational challenge, but also a biological challenge since many of these graphs consist of a large amount of false positives and negatives. In this presentation, I will discuss two applications. The first one is to find the matches of a subgraph pattern in a large biological network with possible missing edges. To solve this problem, an index structure with bloom filter and random spanning trees are developed, which can achieve an efficient matching time. The second application is to infer potential signaling transduction pathways from a protein interaction network. The protein interaction network is first refined by integrating other types of biological information, e.g., gene expression profiles, protein location, etc. A trained model capturing the characteristics of pathway interactions is learned from known signaling pathways. Based on this model, signaling pathways are predicted. This method can produce more accurate prediction than existing methods based on experiments on yeast protein interaction networks.
Dr. Jiong Yang received his Ph.D. degree from UCLA at 1999. After graduation, he joined IBM T. J. Watson research centers as a research staff member. He worked as a visiting assistant professor at UIUC computer science department in 2002. Dr. Yang joined the EECS department at Case Western University in 2004 as the Schroeder assistant professor and now he is an associate professor. Currently, his research is focused on managing and analyzing graph data. He has served on the program committees of various conferences, including KDD, ICDM, SDM, ICDE. Dr. Yang is also an associate editor of the international journal on data mining and bioinformatics. He served as the guest editor of IEEE TKDE special issues on Mining Biological Data and IEEE TKDD special issues on Data Mining and Bioinformatics. Dr. Yang has published more than eighty peer reviewed articles in top conferences and journals. He is a senior member of IEEE.