Rui Jiang received his PhD in automatic control engineering from Tsinghua University in 2002. After working as a postdoctoral research associate in Hong Kong University of Science and Technology (2002-2004) and University of Southern California (2004-2007), he joined the Department of Automation in Tsinghua University in 2007 as an associate professor. Dr. Jiang works in the area of pattern recognition, statistics, machine learning, bioinformatics, and computational systems biology, focusing on developing statistical and machine learning approaches to solve biological problems. He is particular interested in the analysis of biological interaction networks and the identification of genetic risk factors underlying complex diseases from the systems biology point of view.

Tentative Title

Sequence-Based Prioritization of Nonsynonymous Single-Nucleotide Polymorphisms for the Study of Disease Mutations

Abstract

The increasing demand for the identification of genetic variation responsible for common diseases has translated into a need for sophisticated methods for effectively prioritizing mutations occurring in disease-associated genetic regions. In this article, we prioritize candidate nonsynonymous single-nucleotide polymorphisms (nsSNPs) through a bioinformatics approach that takes advantages of a set of improved numeric features derived from protein-sequence information and a new statistical learning model called ¡°multiple selection rule voting¡± (MSRV). The sequence-based features can maximize the scope of applications of our approach, and the MSRV model can capture subtle characteristics of individual mutations. Systematic validation of the approach demonstrates that this approach is capable of prioritizing causal mutations for both simple monogenic diseases and complex polygenic diseases. Further studies of familial Alzheimer diseases and diabetes show that the approach can enrich mutations underlying these polygenic diseases among the top of candidate mutations. Application of this approach to unclassified mutations suggests that there are 10 suspicious mutations likely to cause diseases, and there is strong support for this in the literature.