Understanding transcription factor regulation through machine learning techniques¶
Xin Gao
Associate Professor
King Abdullah University of Science and Technology
Abstract: Transcription factors are an important family of proteins that control the transcription rate from DNAs to messenger RNAs through the binding to specific DNA sequences. Transcription factor regulation is thus fundamental to understanding not only the system-level behaviors of gene regulatory networks, but also the recognition and optimization of the DNA binding sites. In this talk, I will first give an overview to the projects in Structural and Functional Bioinformatics Group at KAUST (http://sfb.kaust.edu.sa), and then focus on our efforts on developing machine learning techniques to understanding transcription factor regulations at both network- and molecule-levels. Specifically, I will talk about how we estimate the kinetic parameters from sparse time-series readout of gene circuits, and how we quantitatively model the relationship between these parameters and the DNA binding sites.
Bio: Dr. Xin Gao is an associate professor of computer science in the Computer, Electrical and Mathematical Sciences and Engineering Division at KAUST. He is also a PI in the Computational Bioscience Research Center at KAUST and an adjunct faculty member at David R. Cheriton School of Computer Science at University of Waterloo, Canada. Prior to joining KAUST, he was a Lane Fellow at Lane Center for Computational Biology in School of Computer Science at Carnegie Mellon University, U.S.. He earned his bachelor degree in Computer Science in 2004 from Computer Science and Technology Department at Tsinghua University, China, and his Ph.D. degree in Computer Science in 2009 from David R. Cheriton School of Computer Science at University of Waterloo, Canada.
Dr. Gao’s research interests are building computational models, developing machine learning techniques, and designing efficient and effective algorithms, with particular focus on applications to key open problems in structural biology, systems biology and synthetic biology. He has co-authored more than 100 research articles in the fields of bioinformatics and machine learning, including Nature Communications, Bioinformatics, TPAMI, Nucleic Acids Research, PLOS Computational Biology, IJCAI, and AAAI.