学科专业
学术讲座：大数据时代下基于网络算法和机器学习的生物信息学研究
时间：2018-11-12 作者：网络部 访问量：
湖南科技大学计算机科学与工程学院将于2018年11月13日下午14:30举行学术讲座，敬请光临！
讲座主题：大数据时代下基于网络算法和机器学习的生物信息学研究
报告人：中国矿业大学 陈兴教授
报告时间：2018.11.13 星期二 下午14:30
报告地点：逸夫楼2楼201会议室
主办：湖南科技大学计算机科学与工程学院
讲座内容：
1、PBMDA (PLOS Computational Biology, 2017, 13(3): e1005455, cited 77 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 134 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 39 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.
4、NRWRH (Molecular BioSystems,2012,8(7):1970-1978, cited 218 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 57 times): We proposed similar nature of drug combinations: principal drugs which obtain synergistic effect with similar adjuvant drugs are often similar and vice versa.
报告人简介：
陈兴，中国矿业大学信息与控制工程学院教授,博士生导师,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。担任多家国际主流杂志的副主编、编委、首席特约编委和审稿人。在中科院一区期刊Nucleic Acids Research、Bioinformatics、PLoS Computational Biology、Briefings in Bioinformatics等发表论文98篇（SCI论文93篇，影响因子累计约410），论文被引用3000余次，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”高层次人才项目等项目。
讲座主题：大数据时代下基于网络算法和机器学习的生物信息学研究
报告人：中国矿业大学 陈兴教授
报告时间：2018.11.13 星期二 下午14:30
报告地点：逸夫楼2楼201会议室
主办：湖南科技大学计算机科学与工程学院
讲座内容：
1、PBMDA (PLOS Computational Biology, 2017, 13(3): e1005455, cited 77 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 134 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 39 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.
4、NRWRH (Molecular BioSystems,2012,8(7):1970-1978, cited 218 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 57 times): We proposed similar nature of drug combinations: principal drugs which obtain synergistic effect with similar adjuvant drugs are often similar and vice versa.
报告人简介：
陈兴，中国矿业大学信息与控制工程学院教授,博士生导师,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。担任多家国际主流杂志的副主编、编委、首席特约编委和审稿人。在中科院一区期刊Nucleic Acids Research、Bioinformatics、PLoS Computational Biology、Briefings in Bioinformatics等发表论文98篇（SCI论文93篇，影响因子累计约410），论文被引用3000余次，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”高层次人才项目等项目。