报告题目：Computational biological hypothesis generation using "-omics" data
报告人：Dr. Peng Yu (Texas A&M University)
Forming biological hypotheses are crucial to the success of scien7fic inves7ga7ons in modern biology and medicine. To generate good biological hypotheses efficiently, computa7onal approaches have been playing increasingly important roles due to the development of high-throughput technologies that enable the produc7on of a vast amount of "-omics" data at a rapidly increasing rate. Despite the fact that most such data are freely available publicly, they are typically not well organized and not annotated consistently, making it difficult for datadriven hypothesis genera7on to catch up with the pace of data genera7on. To address this challenge, we propose a computa7onal hypothesis-genera7on paradigm that is based on systema7c manual cura7on of public datasets. Using the data resource built upon the curated data, we applied our proposed computa7onal framework to find key gene regulators in skin biology, thermogenesis, and neurobiology. A number of candidate genes have been experimentally validated by wetlab experiments and the published literature. The remaining candidates are also good targets for addi7onal experimental valida7on. More importantly, these iden7fied genes may serve as poten7al targets for the related diseases. In summary, our research paves the way for developing more effec7ve automated hypothesis-genera7on methods and will help biologists designing targeted experiments aimed at increasing the speed of meaningful biological discoveries.