Title: Stochastic and Information-theoretic Approaches to Analysis of Biological Data
Speaker: Farzad Farnoud
Abstract: The significant growth in the volume and variety of biological data over the past two decades has created plenty of opportunities for data analytics, with essential applications to biology and medicine. In this talk, I will present our work on aspects of analysis and fusion of biological data, leveraging tools from information theory, machine learning, and stochastic modeling. First, I will present an estimation framework for studying the rates of DNA tandem duplication and substitution mutations by analyzing DNA tandem repeat regions. These regions form about 3% of the human genome and are known to cause several diseases. The proposed method, obtained through a stochastic approximation framework, has smaller estimation error compared to previous work and enables the study of various factors affecting mutation rates through the study of a single genome. Second, I will describe HyDRA, a data fusion tool for gene prioritization, which is the task of computationally identifying genes that are most likely to cause a certain disease. HyDRA relies on novel distances between rankings and rank aggregation methods to combine data from various biological datasets. We show that it achieves better accuracy in identifying disease genes while being more scalable compared to the state-of-the-art methods.
Bio: Farzad Farnoud is a postdoctoral scholar at the California Institute of Technology. He received his MS degree in Electrical and Computer Engineering from the University of Toronto in 2008. From the University of Illinois at Urbana-Champaign, he received his MS degree in mathematics and his PhD in Electrical and Computer Engineering in 2012 and 2013, respectively. His research interests include the information-theoretic and probabilistic analysis of genomic evolutionary processes; rank aggregation and gene prioritization; and coding for flash memory and DNA storage. He is the recipient of the 2013 Robert T. Chien Memorial Award from the University of Illinois for demonstrating excellence in research in electrical engineering and the recipient of the 2014 IEEE Data Storage Best Student Paper Award.