Dec 1, 2017
Lopata Hall, Room 101
Reconstructing Regulatory Networks from scRNA-Seq Data
Dr. Ziv Bar-Joseph
FORE Systems Professor Computational Biology and Machine Learning
Carnegie Mellon University
Generating detailed and accurate organogenesis models using single cell RNA-Seq data remains a major challenge. Additional information beyond expression levels may be needed to accurately determine the correct ordering and branching of progenitor cells and the set of transcription factors (TFs) that are active during the different stages of this process. To enable such modeling we have recently developed a new method based on graphical models which integrates expression similarity with regulatory information to reconstruct the dynamic developmental cell trajectories. In the talk I will discuss application of the method to model lung and heart development and will also present new methods for querying and retrieving scRNA-Seq data.
Ziv Bar-Joseph is the FORE Systems Professor of Computational Biology and Machine Learning at the School of Computer Science at Carnegie Mellon University. His work focuses on the analysis, integration and modeling of high throughput biological data and on improving algorithms for distributed computational networks by relying on our increased understanding of how biological systems operate. Dr. Bar-Joseph received his Ph.D. from MIT in 2003. He was the recipient of the DIMACS-Celera Genomics Graduate Student Award in Computational Biology, the NSF CAREER award and Overton prize in computational biology. He is currently an associate editor of Bioinformatics and the director of the joint CMU-Pitt PhD program in computational biology.