2015.10.12 The Wisdom of the Computational Biology Crowds - From Network Inference to Disease Models

2019-07-11 14:11:54 0

Title:  The Wisdom of the Computational Biology Crowds - From Network Inference to Disease Models


Speaker: Prof. Gustavo Stolovitzky

 IBM Research and Icahn School of Medicine at Mount Sinai


Address: Rm 101, East wing of Old Chemistry Building, Peking Unversity 


Chair:  Prof. Chao Tang,  Center for Quantitative Biology 





   The pace at which biomedical datasets of unprecedented size and complexity are created outstrips the capabilities of the traditional peer-review system and requires new paradigms that more quickly produce verified methods and data-driven findings. To help benchmarking emerging algorithms we created the DREAM Challenges initiative, a crowd sourcing-based approach designed to critically assess complex analytic workflows using the wisdom of crowds, while accelerating the pace of scientific discovery and fostering the creation of communities around pressing biomedical problems.  I will present the results of several community Challenges that cut across the problems that are of most interest in current Computational Biology, ranging from the inference of gene regulatory and signaling networks, through the problem of predicting drug sensitivity and synergy on cell lines, to the problem of predicting disease progression and response to treatment.

报告人简介 Dr. Gustavo Stolovitzky is a Distinguished Research Staff Member at IBM Research where he directs the Translational Systems Biology and Nano-Biotechnology Program. He is also an adjunct Professor at the Icahn School of Medicine at Mount Sinai. Dr. Stolovitzky is a recognized leader in the field of computational biology, where he has pioneered the use of the Wisdom of Crowds as a robust methodology for predictive modeling. He founded and leads the DREAM Challenges, has organized more than 35 scientific Challenges, published over 125 papers and patented more than 20 inventions in the fields of high-throughput biological-data analysis, reverse engineering biological circuits, the mathematical modeling of biological processes and nano-biotechnology.