Transcription factors (TF) are an important family of proteins that control the transcription rate from DNAs to messenger RNAs through the binding to specific DNA sequences. An accurate characterization of TF-DNA binding affinity landscape is crucial to a quantitative understanding of the molecular mechanisms underpinning endogenous gene regulation. In this talk, I will introduce two machine learning methods that we recently developed for modeling TF-DNA binding affinity. The first method is a two-round support vector regression with weighted degree kernel, which can accurately capture important k-mers that contribute to high and low affinity values. In contrast, the second method aims at incorporating both position-specific information and long-range interaction. It is an end-to-end learning framework that combines the strength of graphical models, Hilbert space embedding, and deep learning.
Dr. Xin Gao is an associate professor of computer science in the Computer, Electrical and Mathematical Sciences and Engineering Division at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. 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.