报告时间：2018年11月27日周二16:00
报告地点：湖南大学信息科学与工程学院542会议室
报告人简介：陈兴，中国矿业大学信息与控制工程学院教授（2016-至今，直接破格）,博士生导师（2016-至今）,中国矿业大学生物信息研究所所长,中国矿业大学首批越崎学者，江苏省“六大人才高峰”高层次人才，中国工业与应用数学学会数学生命科学专业委员会秘书长，辽宁省生物大分子计算模拟与信息处理工程技术研究中心专家委员会副主任，江苏省生物信息学专业委员会委员，江苏省人工智能学会智能系统与应用专业委员会委员,江苏省双创团队核心成员。Frontiers in Genetics、Frontiers in Plant Science、BMC Systems Biology三家中科院二区杂志副主编，Scientific Reports、Current Protein & Peptide Science、Current Proteomics三家SCI杂志编委，Frontiers in Microbiology、Current Medicinal Chemistry、International Journal of Molecular Sciences等七家SCI杂志首席特约编委，八家国际生物信息学会议程序委员会成员。从事生物信息学和系统生物学领域的相关研究，主要研究工作集中在开发和利用复杂网络、机器学习、深度学习、图论、组合数学等方法对于复杂疾病、非编码RNA和网络药理学方面的系统生物学领域的多个重要问题进行探索和研究，并取得一系列重要进展。在中科院一区期刊Nucleic Acids Research、Bioinformatics、PLoS Computational Biology、Briefings in Bioinformatics等发表论文100篇（SCI论文95篇，影响因子累计约415），其中第一作者52篇,通讯作者79篇，以一作或者通讯发表JCR一区论文59篇。论文被引用3000多次，目前9篇论文为ESI高被引论文，1篇论文为ESI热点论文，H-因子为29，单篇最高引用次数为441，曾获教育部高等学校科学研究优秀成果奖自然科学奖二等奖、江苏省教育教学与研究成果奖高校自然科学研究类一等奖、淮海科技英才奖、国际网络博弈论大会最佳论文奖、图论与组合算法国际研讨会青年论文奖、世界华人数学家大会新世界数学奖、徐州市自然科学优秀学术论文、徐州市优秀科技工作者、沈阳市自然科学学术成果奖等荣誉，主持国家自然科学基金面上项目、青年基金、江苏省“六大人才高峰”高层次人才项目、中国矿业大学越崎学者人才引进项目等项目。
内容提要：In this talk, the following effective computational models developed by Chen Group for the Bioinformatics research will be introduced: 1) PBMDA (PLOS Computational Biology, 2017, 13(3): e1005455, cited 78 times): Path-Based MiRNA-Disease Association (PBMDA) prediction model was proposed by constructing a heterogeneous graph consisting of three interlinked sub-graphs and further adopting depth-first search algorithm to infer potential miRNA-disease associations. 2) LRLSLDA (Bioinformatics, 2013,29(20):2617-2624, cited 137 times): We proposed the assumption that similar diseases tend to be associated with functionally similar lncRNAs and further developed the method of Laplacian Regularized Least Squares for LncRNA-Disease Association (LRLSLDA) in the semi-supervised learning framework. 3) KATZHMDA (Bioinformatics, 2017, 33(5):733-739, cited 43 times): We constructed a microbe-human disease association network and further developed a novel computational model of KATZ measure for Human Microbe–Disease Association prediction (KATZHMDA) based on the assumption that functionally similar microbes tend to have similar interaction and non-interaction patterns with noninfectious diseases, and vice versa. To our knowledge, KATZHMDA is the first tool for microbe–disease association prediction. 4) NRWRH (Molecular BioSystems,2012,8(7):1970-1978, cited 220 times): The method of Network-based Random Walk with Restart on the Heterogeneous network (NRWRH) is developed to predict potential drug–target interactions on a large scale under the hypothesis that similar drugs often target similar target proteins and the framework of Random Walk. NRWRH makes full use of the tool of the network for data integration to predict drug–target associations. 5) NLLSS (PLOS Computational Biology, 2016,12(7): e1004975, cited 58 times): We proposed similar nature of drug combinations: principal drugs which obtain synergistic effect with similar adjuvant drugs are often similar and vice versa, and further developed a novel algorithm termed Network-based Laplacian regularized Least Square Synergistic drug combination prediction (NLLSS) to predict potential synergistic drug combinations by integrating different kinds of information such as known synergistic drug combinations, drug-target interactions, and drug chemical structures.
