The following Keynote Speech was presented during MULTICONF-09
Computational Molecular Network Analysis: Past, Present, and Future
Fengzhu Sun, Ph.D.
Professor of Computational Biology and Bioinformatics,
University of Southern California
USA
http://www-rcf.usc.edu/~fsun
Brief biography of the Speaker: Dr. Fengzhu Sun has been Professor of Molecular and Computational Biology at the University of Southern California (USC) since 2006. He received PhD degree in Applied Mathematics from USC in 1994 under the direction of Dr. Michael Waterman. He came back to USC in 2000 as an associate professor after being an assistant professor of genetics and biostatistics at Emory University from 1995 to 2000. Professor Sun works in the area of Computational Biology and Bioinformatics, Statistical Genetics, and Mathematical Modeling. His recent research interests include protein interaction networks, gene expression, single nucleotide polymorphisms (SNP), linkage disequilibrium (LD) and their applications in predicting protein functions, gene regulation networks, and disease gene identification. He is also interested in metagenomics, in particular, marine genomics. He has published over 80 peer reviewed papers in the field of computational biology and bioinformatics.
Abstract of the keynote speech: An enormous amount of biological data is available and it is continued to be generated, including genomic sequences, gene expression profiles, protein interactions, gene regulation networks, and genetic polymorphisms. An important problem is the integration of different data sources to solve biological problems of interest. Our philosophy is that different data will give us some but not all the information about the biological problems. By combining different problems intelligently, we are able to obtain a more complete picture of the problems of interest. We will use the following three examples to show our points, a). Protein function prediction combining different data sources, b) predicting domain interactions from protein interactions, and c) inference of causal genes and pathways for particular phenotypes.