4月22日交叉学科论坛学术报告（曹沁香港中文大学，邹斌香港城市大学）

4月22日交叉学科论坛学术报告（曹沁香港中文大学，邹斌香港城市大学）

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报告题目1： Reconstruction of enhancer–target networks in 935 samples of human primary cells, tissues and cell lines

报告时间： 2018年4月22日上午9:50（周日）
报告地点：计算机学院B403
报告人：曹沁
报告人单位：香港中文大学

报告人简介：
曹沁，2017年博士毕业于香港中文大学计算机科学与工程系，2013年本科毕业于外围投注365，现为香港中文大学大学博士后研究员。研究方向主要是生物信息学。目前已发表学术论文4篇,其中一篇以第一作者发表于Nature Genetics (SCI一区期刊，Nature子刊，影响因子31)。获得的奖项有香港科学会颁发的香港青年科学家奖（第三名）、美国RECOMB/ISCB Regulatory Systems Genomics 协会颁发的2016-2017年度十大论文奖、香港中文大学计算机科学与工程系优秀助教（两次）、香港政府博士奖学金。

报告摘要：
We propose a new method for determining the target genes of transcriptional enhancers in specific cells and tissues. It combines global trends across many samples and sample-specific information, and considers the joint effect of multiple enhancers. Our method outperforms existing methods when predicting the target genes of enhancers in unseen samples, as evaluated by independent experimental data. Requiring few types of input data, we are able to apply our method to reconstruct the enhancer–target networks in 935 samples of human primary cells, tissues and cell lines, which constitute by far the largest set of enhancer–target networks. The similarity of these networks from different samples closely follows their cell and tissue lineages. We discover three major co-regulation modes of enhancers and find defense-related genes often simultaneously regulated by multiple enhancers bound by different transcription factors. We also identify differentially methylated enhancers in hepatocellular carcinoma (HCC) and experimentally confirm their altered regulation of HCC-related genes.

报告题目2：用计算的方法分析非小分子肺癌对药物的选择性

报告时间： 2018年4月22日上午10:45（周日）
报告地点：计算机学院B403
报告人：邹斌
报告人单位：香港城市大学

报告人简介：
邹斌，2017年博士毕业于香港城市大学电子工程系，2014年硕士毕业于外围投注365，2012年本科毕业于北京邮电大学国际学院，现为香港城市大学高级副研究员。研究方向主要是：结构生物学，计算生物学。目前已发表学术论文十余篇。

报告摘要：
Non-small cell lung cancer (NSCLC) with activating EGFR mutations, especially exon 19 deletions and the L858R point mutation, is particularly responsive to gefitinib and erlotinib. However, the sensitivity varies for less common and rare EGFR mutations. To decode the drug sensitivity/selectivity of EGFR mutants, it is important to analyze the interaction between EGFR mutants and EGFR inhibitors. We used the technique of protein-ligand interaction fingerprint (IFP) to analyze and compare the binding modes of EGFR mutant-gefitinib/erlotinib complexes. Molecular dynamics simulations were employed to obtain the dynamic trajectory and a matrix of IFPs for each EGFR mutant-inhibitor complex. Multilinear Principal Component Analysis (MPCA) was applied for dimensionality reduction and feature selection. The selected features were further analyzed for use as a drug sensitivity predictor. The results showed that the accuracy of prediction of drug sensitivity was very high for both gefitinib and erlotinib. We can conclude that the computational methods are useful to predict the drug sensitivity of NSCLC with EGFR mutants.

上一篇：4月22日交叉学科论坛学术报告（Dr Shirui Pan, University of Technology Sydn

下一篇：2018年4月24日学术报告（Zhaofeng Su，University of Sydney）