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Computational biological hypothesis generation using "-omics" data
2017-08-30 | 【 【打印】【关闭】

  报告人:Dr. Peng Yu Dept. Electrical and Computer Engineering & TEES-AgriLife Center for Bioinformatics and Genomic Systems

  Engineering (CBGSE), Texas A & M University

  报告时间:10:00-12:00,Aug 30th, Wednesday

  报告地点:Room 801, 8th Floor, ICT

  报告摘要:

  Forming biological hypotheses are crucial to the success of scientific investigations in modern biology and medicine. To generate good biological hypotheses efficiently, computational approaches have been playing increasingly important roles due to the development of high-throughput technologies that enable the production 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 data-driven hypothesis generation to catch up with the pace of data generation. To address this challenge, we propose a computational hypothesis-generation paradigm that is based on systematic manual curation of public datasets. Using the data resource built upon the curated data, we applied our proposed computational framework to find key gene regulators in skin biology, thermogenesis, and neurobiology.

  A number of candidate genes have been experimentally validated by wet-lab experiments and the published literature. The remaining candidates are also good targets for additional experimental validation. More importantly, these identified genes may serve as potential targets for the related diseases. In summary, our research paves the way for developing more effective automated hypothesis-generation methods and will help biologists designing targeted experiments aimed at increasing the speed of meaningful biological discoveries.

  报告人简介:

  Dr. Peng Yu received his bachelor degree from Peking University, and received his Ph. D. degree from the University of Texas. Currently he is working in Dept. Electrical and Computer Engineering & TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering (CBGSE), Texas A & M University. His research interests include bioinformatics, especially the computational methods to facilitate biologists in forming and testing hypotheses.

 
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