2014 Poster Sessions : Integrative Models of Expression and Chromatin Variation Across Cell Types

Student Name : Sofia Kyriazopoulou-Panagiotopoulou
Advisor : Serafim Batzoglou
Research Areas: Artificial Intelligence
Although all cells in our body contain the same genetic material in their DNA, they can perform vastly different functions, by selectively expressing subsets of their genes. Cell-type-specific gene regulation is achieved through an interplay between regulatory proteins, such as transcription factors, and epigenetic mechanisms, which affect the higher level organization of the genome. The interactions between these regulatory components and their dependence on DNA sequence information are only partially characterized. A better understanding of condition-specific regulatory mechanisms is important for understanding the causes of genetic diseases and for identifying potential targets for intervention. We developed a novel machine learning framework for discovering cell-type-specific rules of regulation based on both the expression patterns of regulators and DNA sequence information. Our method uses boosting and multi-task learning to learn cell-type-specific and shared regulatory programs and to efficiently handle the complex feature space of potential regulatory interactions. Unlike previous work in this field, our model incorporates the effect of the cell-type-specific activity of distal regulatory elements, such as enhancers, and takes advantage of prior knowledge regarding protein interactions. Using large-scale datasets from the Roadmap Epigenomics and ENCODE Projects, we constructed a regulatory map of a large number of human tissues. Our model achieves high predictive power and discovers both known and novel cell-type-specific regulators and context-specific interactions between them.

Sofia is a PhD student in the Computer Science Department at Stanford working with Professors Serafim Batzoglou and Anshul Kundaje. Her research focuses on developing machine learning algorithms to model gene regulation in humans. More broadly, Sofia is interested in integrating large genomic datasets to study the variation in gene expression and chromatin state across tissues, individuals, or species. At Stanford, Sofia has served as a teaching assistant for classes in machine learning, algorithms, and computational genomics. She has been awarded fellowships from the Siebel Foundation and from the Stanford Center for Computational, Evolutionary, and Human Genomics. Sofia received her B.Sc. in Computer Science from the Athens University of Economics and Business, where she did research on statistics and databases.