姜涛，中心首席科学家，现为河滨加州大学（University of California, Riverside）计算机系教授，他还曾执教于加拿大的McMaster大学（McMaster University）。姜教授现在还同时兼任清华大学计算机系访问教授（长江学者），以及北航客座教授。他于2007年获选美国计算机协会院士（ACM Fellow）（Citation: “For contributions to computational biology and computational complexity”，汉译：“对计算生物学和计算复杂性研究做出贡献”）。
姜教授1984年于中国科大计算机系学士毕业，1988年美国明尼苏达大学（Unvisity of Minnesota）计算机系博士毕业。姜教授的研究方向包括算法设计和分析，计算分子生物学，和有限自动机复杂性等。他属于多个国际期刊的编委会，如Journal of Combinatorial Optimization，Journal of Computer Science and Technology等，并参与组织多个学术会议。姜教授的博士毕业生有Lusheng Wang，Bhaska DasGupta，Todd Wareham，Lan Liu，Zheng Fu等。2017 11 3 湖南师范大学
报告题目：Toward More Sensitive Differential Expression Analysis on RNA-Seq Data 报告人： Tao Jiang教授，Department of Computer Science and Engineering University of California, Riverside, and School of Information Science and Technology, Tsinghua University 报告时间：2017年11月30日10：00 报告地点：量子楼410报告厅 As a fundamental tool for discovering genes involved in a disease or biological process, differential gene expression analysis plays an important role in genomics research. High throughput sequencing technologies such as RNA-Seq are increasingly being used for differential gene expression analysis that was dominated by the microarray technology in the past decade. However, inferring differentially expressed genes based on the observed difference of RNA-Seq read counts has unique challenges that were not present in microarray-based analysis. An RNA-Seq based differential expression analysis may be biased against genes with low read counts since the difference between genes with high read counts is more easily detected. Moreover, analyses that do not take into account alternative splicing often miss genes that have differentially expressed transcripts. In this talk, we introduce two novel methods for enhancing differential expression analysis. One uses a markov random field (MRF) model to integrate RNA-Seq data with coexpression data and the other represents independent alternative splicing events by decomposing the splice graph of a gene into special modules (called alternative splicing modules or ASMs). Our extensive experiments on simulated data and real data with qPCR validation demonstrate that these enhancements lead to more sensitive differential expression analyses and better classification of cancer subtypes, cell types and cell-cycle phases. 姜教授首先简要介绍了报告的大背景以及本次讲座内容所涉及的相关生物知识，指出RNA-Seq等检测手段的广泛应用，并指出这些测序手段存在的不足，从而提出用MRF模型应用到检测差异表达的基因，并利用共同表达数据，提高统计效果。此外，此次研究中，姜教授还进一步证明了MRF模型较之前的检测手段更为准确、偏差更小，从而论证了新型手段的可行性。接着，姜教授还向大家指出了生物信息和数据挖掘的区别，生物信息是需要结合真实数据进行的实用性较强的科学，而不能仅仅通过数据测试妄下结论。姜教授从常用方法出发，通过常用方法举例，结合仿真实验指出不足，再引出新的方法，并通过和常用方法的比较，进一步论证新方法的可行性。为我院师生论文写作的思路方法提供了很好的借鉴。