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【作者: | 发布日期:2022-04-21 | 浏览次数:】

报告题目: Gene Regulation Inference using High Throughput Sequencing Data

告 人:刘丙强

报告摘要:Reconstruction and analysis of transcriptional regulatory networks is a key to understand the intrinsic mechanism of the life. Currently, numbers of challenging questions in this research area need to be answered such as how the TFs regulate genes, how the genes be organized in transcription, and so on. High throughput sequencing data provides unprecedented opportunities to overcome these difficulties. Meanwhile, it also brings new computational and modeling challenges in high-dimensional data mining and heterogeneous data integration. To infer gene expression regulation mechanisms, we developed a series of computational frameworks focusing on several important computational problems including regulatory motif finding, regulon prediction, transcriptional unit prediction etc., both on bacterial and human genomes. These studies provided fundamental knowledge to guide the reconstruction and analysis of transcriptional regulatory networks, improved our understanding of how gene expression is controlled by the underlying regulatory systems, and have promising potentials on the research of regulatory mechanisms underlying complex diseases.


报告时间: 2022-04-22 7:00-8:30

报告地点: 腾讯会议:116961853