Tao Jiang received B.S. in Computer Science and Technology from the University of Science and Technology of China, Hefei, inJuly 1984 and Ph.D. in Computer Science from University of Minnesota in Nov. 1988. He was a faculty member at McMaster University, Hamilton, Ontario, Canada during Jan. 1989 - July 2001 and is now Professor of Computer Science and Engineering at University of California - Riverside (UCR).
He is also a member of the UCR Institute for Integrative Genome Biology, a member of the Center for Plant Cell Biology, a principal scientist at Shanghai Center for Bioinformation Technology, and Qianren Chair Visiting Professor at Tsinghua University. Tao Jiang's recent research interestincludes combinatorial algorithms, computational molecular biology, bioinformatics, and computational aspects of information retrieval.
He is a fellow of the Association for Computing Machinery (ACM) and of the American Association for the Advancement of Science (AAAS), and held a Presidential Chair Professor position at UCR during 2007-2010.
He has published over 260 papers in computer science and bioinformatics journals and conferences, and won several best paper awards.
More information about his work can be found at http://www1.cs.ucr.edu/~jiang
Transcript-Based Differential Expression Analysis for Population RNA-Seq Data
Differential transcript expression (DTE) analysis on population data without predefined conditions is critical to many biological or clinical studies. For example, it can be used to discover biomarkers to classify cancer samples into previously unknown subtypes such that better diagnosis and therapy methods can be developed for the subtypes. Although several DTE tools have been published, these tools either assume binary conditions in the input population or require the number of conditions to be given.
In this work, we propose a novel DTE analysis algorithm, called SDEAP, that estimates the number of conditions directly from the input samples using Dirichlet mixture models and discovers alternative splicing events using a new graph modular decomposition algorithm.
By taking advantage of the above technical improvement, SDEAP was able to outperform the other DTE analysis methods in our extensive experiments on simulated data and real data with qPCR validation. The prediction of SDEAP also allowed us to classify the samples of cancer subtypes and cell-cycle phases more accurately.
This is joint work with Ei-Wen Yang at UCR/UCLA.