Keynote Speakers
Vineet Bafna
University of California, San Diego, USA
Keynote Speaker
Vineet Bafna
Vineet Bafna is Professor in the Computer Science Department, and Director of the Bioinformatics and Systems Biology Program at UC San Diego. Prior to joining UCSD in 2003, he spent seven years in the bio-science industry, ultimately as Director of Informatics Research at Celera Genomics. At Celera, he participated in the human genome project, designing novel tools for gene discovery, and leading the analysis of mass spectrometry data for identifying cancer bio-markers. His current research focus is on computational problems in Population Genomics, Cancer Genomics, and Proteomics. He an Associate Editor for Biology Direct, Bioinformatics, Algorithms for Molecular Biology, and has served on the program committees of ISMB, RECOMB, and other Bioinformatics conferences. He has co-authored over 120 research articles in refereed journals and conference proceedings.
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Title: Identifying the favored allele in a selective sweep.
Abstract:
Selection is a dominant force in evolution. Mutations arising at random might favor individuals in a specific environmental niche, and populations adapt by rapidly increasing the frequency of individuals carrying the favored mutations. The selection process results in distinct patterns (a signature) of allele frequencies and haplotype structures that can be exploited to identify the genes responding to selection pressure. A study of selection signals in humans has led to molecular insight into the evolution of many natural traits such as skin and eye color, as also adaptation to extreme environments.
Computational methods that scan population genomics data to identify signatures of selective sweep have been actively developed, but mostly do not identify the specific mutation favored by the selective sweep. In this talk, we describe an approach that uses population genetics and machine learning techniques to pin-point the favored mutation, even when the signature of selection extends to 5Mbp. Our method, iSAFE, was tested extensively on simulated data and 22 known sweeps in human populations using the 1000 genome project data with some evidence for the favored mutation. iSAFE ranked the candidate mutation among the top 15 (out of ~21,000 candidates) in 14 of the 22 loci, and identified previously unreported mutations as favored the 5 regions.
David Zhang
Hong Kong Polytechnic University, Hong Kong
Keynote Speaker
David Zhang
David Zhang graduated in Computer Science from Peking University. He received his MSc in 1982 and his PhD in 1985 in both Computer Science from the Harbin Institute of Technology (HIT), respectively. From 1986 to 1988 he was a Postdoctoral Fellow at Tsinghua University and then an Associate Professor at the Academia Sinica, Beijing. In 1994 he received his second PhD in Electrical and Computer Engineering from the University of Waterloo, Ontario, Canada. Currently, he is a Chair Professor at the Hong Kong Polytechnic University where he is the Founding Director of Biometrics Research Centre (UGC/CRC) supported by the Hong Kong SAR Government in 1998. He also serves as Visiting Chair Professor in Tsinghua University and HIT, and Adjunct Professor in Shanghai Jiao Tong University, Peking University, National University of Defense Technology and the University of Waterloo. He is the Founder and Editor-in-Chief, International Journal of Image and Graphics (IJIG); Book Editor, Springer International Series on Biometrics (KISB); Organizer, the first International Conference on Biometrics Authentication (ICBA); Associate Editor of more than ten international journals including IEEE Transactions; Technical Committee Chair of IEEE SMC and the author of more than 15 books and over 400 international journal papers. He has been listed as a Highly Cited Researchers in Engineering by Thomos Reters in 2014, 2015 and 2016, respectively. Professor Zhang is a Croucher Senior Research Fellow, Distinguished Speaker of the IEEE Computer Society, and a Fellow of both IEEE and IAPR.
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