Discovering meaningful interaction pathways in complex biological interaction networks is one of the important goals of the post-genomic era. Following breakthroughs in biotechnology, a wealth of experimentally determined biomolecular interaction data is becoming available. It is important to develop computational methods that go beyond pair-wise interaction analysis to help in the analysis of such data. In recognition of this, we are developing k-MinPaF, an approach to find and interpret the first few shortest paths in complex biological interaction networks. The talk shall outline the framework and present a preliminary implementation on data from a protein interaction database.
Meeyoung Park is a Ph.D. student in the School of Computing and Engineering at the University of Missouri-Kansas city. Her main research interests include Bioinformatics, Machine Learning, Data Mining and the Semantic Web. She received her B.S. degree in Computer Science & Statistics and an M.S. degree in Computer Science from Chonnam National University, Korea. She is the recipient of a grant from the UMKC Women's Council Graduate Assistance Fund.