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Machine Learning in High-Throughput Genomics and Proteomics
Speaker:Xuegong Zhang
Affiliation: Tsinghua University
Description: High-throughput genomics and proteomics data have been a major source of information in the current systems biology investigations. Machine learning methods like support vector machines (SVMs), neural networks, dimension reduction etc. have been playing an active role the analysis and mining of these data, composing one of the major efforts in current bioinformatics research. A typical scenario is using gene expression data obtained with DNA microarrays or proteomics data obtained with mass-spectrometry for classifying the samples (e.g. normal vs. cancer, subtypes of the cancer, etc) and discovering the relevant genes behind the classification. This tutorial will provide a systematic and in-depth overview of this field, covering from the biological background and major issues to be studied to state-of-the-art approaches and representative application examples, as well as some common pitfalls, open questions and challenges. The focus will be on clustering, classification, gene/biomarker selection and the proper assessment of the results achieved with machine-learning approaches.
Presenter biography: Xuegong Zhang received his BS degree in Industrial Automation at Tsinghua University in 1989, and his Ph.D. degree in Pattern Recognition and Intelligent Systems at Tsinghua University in 1994. He joined the faculty of Tsinghua University, Department of Automation as an Assistant Professor in 1994, Associate Professor in 1996 and Full Professor in 2002. During 2001-2002, Dr. Zhang worked at Harvard School of Public Health as a visiting scientist. He is now the director of the Bioinformatics Division of TNLIST (Tsinghua National Laboratory for Information Science and Technology), Professor of Pattern Recognition and Bioinformatics of the Department of Automation, Tsinghua University. His current research interests include the analysis of high-throughput biological data with machine learning methods, computational analysis of alternative splicing and microRNAs, computational analysis of haplotype blocks and meiotic recombination hotspots, and statistical learning methodology
