報告題目：基于深度學習模型的生物序列分析
Biological Sequence Analyses Based on Deep Learning Methods
報告時間：2018年12月19日上午9：30
報告地點：伟德国际BETVlCTORA521
報 告 人：許東 美國密蘇裡大學 James C.Dowell教授
報告人簡介：Dong Xu is Shumaker Endowed Professor in Department of Electrical Engineering and Computer Science, Director of Information Technology Program, with appointments in the Christopher S. Bond Life Sciences Center and the Informatics Institute at the University of Missouri-Columbia. He obtained his PhD from the University of Illinois, Urbana-Champaign in 1995 and did two years of postdoctoral work at the US National Cancer Institute. He was a Staff Scientist at Oak Ridge National Laboratory until 2003 before joining the University of Missouri, where he served as Department Chair of Computer Science during 2007-2016. His research is in computational biology and bioinformatics, including machine-learning application in bioinformatics, protein structure prediction, post-translational modification prediction, high-throughput biological data analyses, in silico studies of plants, microbes and cancers, biological information systems, and mobile App development for healthcare. He has published more than 300 papers. He was elected to the rank of American Association for the Advancement of Science (AAAS) Fellow in 2015.
報告内容簡介：We have applied deep learning in several analysis and prediction problems for biological sequences, including prediction of protein secondary and super-secondary structures, protein domain partition, protein localization prediction, protein post-translational modification site prediction, and genotype-phenotype relationship. These applications utilized a broad spectrum of deep learning methods, including Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent neural network (RNN), Generative Adversarial Network (GAN) and Capsule Network. Various network architectures are also explored, including residual network, inception network, dense network, etc. Some of these applications represent novel formulations of the problems, while others significantly improved the performance over the previous methods. These studies also addressed some important deep-learning issues, such as handling small data, using transfer learning to pretrain models, and making the models transparent and explainable.
主辦單位：
伟德国际BETVlCTOR
伟德国际BETVlCTOR軟件學院
伟德国际BETVlCTOR計算機科學技術研究所
符号計算與知識工程教育部重點實驗室
