Bio-relation Discovery via Sparse Learning
报告人:石毅博士(University of Alberta, Canada)
时 间:5月14日(周一)下午2:00
地 点:同济大学南校区实训楼纳米院219会议室

In this work, I discuss several important problems in the area of bio-relation discovery. Discovering bio-relations is an important problem that arises frequently in bioinformatics. It involves identifying relationships (usually pairwise) between bio-entities. These relationships can be categorized as undirected versus directed. I will investigate both types of BRD problems in this dissertation. For undirected BRD, I will specifically discuss the gene-sample expression bi-clustering problem. For directed BRD, I will focus on problems in gene regulatory network inference and drug-target network inference. For all these problems I have investigated both heuristic approaches and sparse learning based approaches from machine learning.



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