學科專業
學術講座：大數據時代下基于網絡算法和機器學習的生物信息學研究
時間：2018-11-12 作者：網絡部 訪問量：
2003网站太阳集团将于2018年11月13日下午14:30舉行學術講座，敬請光臨！
講座主題：大數據時代下基于網絡算法和機器學習的生物信息學研究
報告人：中國礦業大學 陳興教授
報告時間：2018.11.13 星期二 下午14:30
報告地點：逸夫樓2樓201會議室
主辦：2003网站太阳集团
講座内容：
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.
報告人簡介：
陳興，中國礦業大學信息與控制工程學院教授,博士生導師,中國礦業大學生物信息研究所所長,中國礦業大學首批越崎學者，江蘇省“六大人才高峰”高層次人才，中國工業與應用數學學會數學生命科學專業委員會秘書長，遼甯省生物大分子計算模拟與信息處理工程技術研究中心專家委員會副主任，江蘇省生物信息學專業委員會委員，江蘇省人工智能學會智能系統與應用專業委員會委員,江蘇省雙創團隊核心成員。擔任多家國際主流雜志的副主編、編委、首席特約編委和審稿人。在中科院一區期刊Nucleic Acids Research、Bioinformatics、PLoS Computational Biology、Briefings in Bioinformatics等發表論文98篇（SCI論文93篇，影響因子累計約410），論文被引用3000餘次，曾獲教育部高等學校科學研究優秀成果獎自然科學獎二等獎、江蘇省教育教學與研究成果獎高校自然科學研究類一等獎、淮海科技英才獎、國際網絡博弈論大會最佳論文獎、圖論與組合算法國際研讨會青年論文獎、世界華人數學家大會新世界數學獎、徐州市自然科學優秀學術論文、徐州市優秀科技工作者、沈陽市自然科學學術成果獎等榮譽，主持國家自然科學基金面上項目、青年基金、江蘇省“六大人才高峰”高層次人才項目等項目。
講座主題：大數據時代下基于網絡算法和機器學習的生物信息學研究
報告人：中國礦業大學 陳興教授
報告時間：2018.11.13 星期二 下午14:30
報告地點：逸夫樓2樓201會議室
主辦：2003网站太阳集团
講座内容：
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.
報告人簡介：
陳興，中國礦業大學信息與控制工程學院教授,博士生導師,中國礦業大學生物信息研究所所長,中國礦業大學首批越崎學者，江蘇省“六大人才高峰”高層次人才，中國工業與應用數學學會數學生命科學專業委員會秘書長，遼甯省生物大分子計算模拟與信息處理工程技術研究中心專家委員會副主任，江蘇省生物信息學專業委員會委員，江蘇省人工智能學會智能系統與應用專業委員會委員,江蘇省雙創團隊核心成員。擔任多家國際主流雜志的副主編、編委、首席特約編委和審稿人。在中科院一區期刊Nucleic Acids Research、Bioinformatics、PLoS Computational Biology、Briefings in Bioinformatics等發表論文98篇（SCI論文93篇，影響因子累計約410），論文被引用3000餘次，曾獲教育部高等學校科學研究優秀成果獎自然科學獎二等獎、江蘇省教育教學與研究成果獎高校自然科學研究類一等獎、淮海科技英才獎、國際網絡博弈論大會最佳論文獎、圖論與組合算法國際研讨會青年論文獎、世界華人數學家大會新世界數學獎、徐州市自然科學優秀學術論文、徐州市優秀科技工作者、沈陽市自然科學學術成果獎等榮譽，主持國家自然科學基金面上項目、青年基金、江蘇省“六大人才高峰”高層次人才項目等項目。