演讲嘉宾--曹志伟

曹志伟
曹志伟
研究学习经历:

1995年获南开大学生化与分子生物学专业本科,1998年获南京大学生物化学专业硕士,2002毕业于新加坡国立大学,获得计算生物学/生物信息专业博士学位。2004加盟上海生物信息技术研究中心。2007年进入同济大学生命科学与技术学院, 任教授至今, 其中 2009-2014年任学院副院长。中华中医药学会中医药信息分会副主任委,上海生物信息学会理事。曹志伟主要发展生物信息学新方法,研究多组分用药作用机制,以及抗体抗原特异识别规律等。已承担多项973、863和自然科学基金资助的国家项目以及上海市项目十余项,获得发明专利2项。发表SCI文章80余篇,他引1800余次。先后获得上海市曙光学者,浦江人才,和教育部新世纪优秀人才计划 支持。

近期发表论文:
  1. Sun Y, Sheng Z, Ma C, Tang K, Zhu R, Wu Z, Shen R, Feng J, Wu D, Huang D, Huang D, Fei J, Liu Q, Cao Z*, Combining genomic and network characteristics for extended capability in predicting synergistic drugs for cancer, Nature Communications, 2015, Sep 28;6:8481. doi: 10.1038/ncomms9481.
  2. Qi Tao, Qiu T, Zhang Q, Tang K, Fan Y, Qiu J, Wu D, Zhang W, Chen Y, Gao J, Zhu R, Cao ZW.*  SEPPA 2.0--more refined server to predict spatial epitope considering species of immune host and subcellular localization of protein antigen. Nucleic Acids Res. 2014 Jul;42(Web Server issue):W59-63. doi: 10.1093/nar/gku395. Epub 2014 May 16.
  3. Sun Y, Zhu R, Ye H, Tang K, Zhao J, Chen Y, Liu Q, Cao Z*. Towards a bioinformatics analysis of anti-Alzheimer's herbal medicines from a target network perspective. Brief Bioinform. 2013 May;14(3):327-43.
  4. Hao Ye, Li Ye,Hong Kang,Duanfeng Zhang,Lin Tao,Kailin Tang, Xueping Liu,Ruixin Zhu,Qi Liu, Y. Z. Chen, Yixue Li and Zhiwei Cao*, HIT: linking herbal active ingredients to targets,  Nucl. Acids Res. 2011 Jan;39:D1055-9. Sun J, et al. Cao ZW*. Nucleic Acids Res. 2009 Jul 1;37(Web Server issue):W612-6.
  5. Sun J, Wu D, Xu T, Wang X, Xu X, Tao L, Li YX, Cao ZW*. SEPPA: a computational server for spatial epitope prediction of protein antigens. Nucleic Acids Res. 2009 Jul 1;37(Web Server issue):W612-6. Epub 2009 May 22.
  6. J. Jia, F. Zhu, X.H. M  a, Z.W. Cao, Y.X. Li and Y.Z. Chen, Mechanisms of drug combinations from interaction and network perspectives.Nat. Rev. Drug Discov., 2009 Feb;8(2):111-28.
会议报告摘要:
Searching synergistic drug combinations to treat cancer(4月23日,11:20 - 11:45 pm)

Synergistic drug combinations have long been highly desired in treating complex disease of cancer. Yet, prioritizing synergistic agents from the large pool of potential combinatorial drugs is highly challenging. In-silico models are thus in urgent need to evaluate and prioritize the best candidate combinations. Here we show that, target network and gene expression profiles can be utilized to construct effective computational models as the Ranking-system of Anti-Cancer Synergy (RACS), which has been validated with outstanding performance on different cancer types. For instance, during the validation on the human -cell lymphoma Ly3 cell line data of NCI-DREAM, the concordance of the prediction result of RACS reached 78%, which significantly improved the performance of the best method previously published as 61% (Random picking:50%: the best result: 90% concordance). Application RACS to clinical patients showed promising outcomes.

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第四届肿瘤基础和转化医学前沿国际研讨会