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演讲摘要：Modern 3C-based techniques facilitate the accurate identification of long-range contacts genome-wide. However, these assays are technically challenging, expensive, and time consuming, making it difficult to investigate functional enhancer mediated loops. In this talk, we introduce an ensemble machine learning model LoopPredictor to predict functional enhancer-mediated loops for cell types which lack 3D conformation information. LoopPredictor is able to efficiently identify cell type-specific enhancer mediated loops, and promoter-promoter interactions, with a modest feature input requirement. LoopPredictor enables the dissection of cell type-specific long-range gene regulation, and can accelerate the identification of distal disease-associated risk variants.
讲者简介：中南大学计算机学院教授、博士生导师、副院长，主要从事生物信息学与数据挖掘研究，主持国家自然科学基金重点项目、优秀青年项目、面上等国家和省部级科研课题十余项。担任湖南省人工智能与医学大数据国际联合实验室主任、生物信息学湖南省重点实验室副主任、中国计算机学会生物信息学专委会委员、中国人工智能学会-生物信息学与人工生命专业委员会常务委员等学术职务，担任《Current Protein & Peptide Science》、《International Journal of Data Mining and Bioinformatics》、《Interdisciplinary Sciences: Computational Life Sciences》等期刊编委，担任ISBRA2019、ISBRA2017、ICPCSEE2017等国际会议的程序委员会主席。
第五届中国计算机学会生物信息学会议
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