赵兴明——同济大学电子与信息工程学院教授

专家信息：

赵兴明，男，同济大学电子与信息工程学院教授，同济大学智能感知网高等研究院研究员，博士生导师。 

教育及工作经历：

2000年，吉林大学工学学士学位。

2003年，吉林大学工学硕士学位。

2005年，获中国科技大学模式识别与智能系统专业博士学位。

2004年至2005年，在香港浸会大学计算机系作Research Assistant。

2006年至2008年，在东京大学作研究员。

2010年6月-2011年12月，在德国欧洲分子生物学实验室(EMBL)做博士后研究。

现任同济大学电子与信息工程学院教授，同济大学智能感知网高等研究院研究员。

学术兼职及社会任职：

1. IEEE Senior Member委员。

2. IEEE SMC Technical Committee on Systems Biology委员。

3. IAPR TC20 (Technical Committee on Pattern Recognition for Bioinformatics) 委员会，委员。

4. 中国运筹学会计算系统生物学分会理事。

5. 中国细胞学会功能基因组信息学与系统生物学分会理事。

6. 上海市生物信息学协会理事。

6. Neurocomputing、Journal of Theoretical Biology和BioMed Research International等多个国际杂志的客座编辑和编委。

Editorships and Editorial Boards:

1. Lead Guest Editor, BioMed Research International (IF=2.9, 2013-)

2. Lead Guest Editor, IET Systems Biology (IF=1.3, 2013-)

3. Lead Guest Editor, The Scientific World Journal (IF=1.6, 2012-)

4. Guest Editor, Journal of Theoretical Biology (IF=2.4, 2013-)

5. Guest Editor, Current Bioinformatics (IF=1.6, 2012-)

6. Academic Editor, Journal of Scientific Research and Reports (2013-)

7. Editorial board member, Journal of Bioinformatics in Nutrition (2012-)

8. Editorial board member, Computational Biology Journal (2012-)

9. Editorial board member, The Open Access Journal of Science and Technology (OAJoST) (2012-) 

Service at Professional Society:

1. Senior Member, IEEE

2. Member, IEEE SMC Technical Committee on Systems Biology

3. Member, IAPR Technical Committee on Pattern Recognition for Bioinformatics

4. Member, CAAI Society of Bio-informatics and Artificial Life

5. Member, International Society for Computational Biology

6. Member, Shanghai Bioinformatics Society

7. Member, Chinese Computational Systems Biology Society

8. Member, Program Committee, InCoB2013 (12th International Conference on Bioinformatics of the Asia-Pacific Bioinformatics Network)

9. Member, Program Committee, ICTBI2012 (The International Conference on Translational Biomedical Informatics)

10. Member, Program Committee, International Conference on Intelligent Computing, 2009-2013

11. Member, Program Committee, IEEE BIBM Conference, 2010-2012

12. Program Chair, 8th IAPR International Conference on Pattern Recognition in Bioinformatics

13. Session Chair, Bio-Molecular Network session, 48th IEEE Conference on Decision and Control, 2009

14. Session Chair, Systems Biology session, The 2009 IEEE International Conference on ystems, Man, and Cybernetics, 2009

主讲课程：

资料更新中…… 

培养研究生情况：

所指导学生已经分赴法国昂热大学、日本东京大学、香港中文大学和华为等单位深造和工作，并先后获得国家奖学金和国际会议最佳学术论文等多种荣誉。

招聘情况：

欢迎对机器学习、数据挖掘、复杂网络、计算系统生物学和最优化感兴趣的同学和同仁加入我们组。

研究方向：

1. 计算系统生物学：疾病基因的预测、药物靶标的预测、药物重定位和生物分子网络的推断等。

2. 图论与最优化：线性规划、图着色、图游走与标记传播等。

3. 文本挖掘：生物医药文献的深度挖掘与知识发现和语义网络的构建等。

承担科研项目情况：

主持完成国家863课题1项，上海市教委创新基金1项，上海市青年科技启明星计划1项，以及国家重点实验室开放课题等多项课题。目前主持在研国家自然科学基金2项、上海市教委创新基金重点项目1项，中-法“蔡元培”合作交流项目1项，美国孟山都合作项目1项等多项课题。

Construction and analysis of molecular networks, including protein - protein interaction network and gene regulatory networks.

Recognition of disease associated molecular pathways and gene modules.

Drug - protein interaction, drug - drug interactions and the prediction of drug combination

科研成果：

1. 在PLoS Computational Biology、Bioinformatics、Current Opinion in Biotechnology、Neucleic Acids Research、Plant Physiology、Journal of Proteome Research、Neural Networks和Pattern Recognition等国际著名学术期刊发表学术论文60余篇，其中SCI检索论文50余篇。参与编写专著3部。

2. 在药物重新定位的研究过程中，应当系统地综合考虑疾病产生的分子机制及其所涉及和影响的分子网络特征，以及药物所影响的分子网络特征，这将会大大提高药物重新定位的准确率。在过去的3年里，赵兴明和他的团队针对“药物重新定位”开展了深入的研究，最近他在乳腺癌药物重定位方面取得了很大进展，他针对乳腺癌所识别的药物，不仅在多个乳腺癌细胞细胞系上得到了验证，而且可以很好地抑制小鼠体内的肿瘤生长。

resource： 

EFG 

2013,22,Jun 

eFG (ebook for Fusarium Graminearum) is an information query system for the pathogenic fungus Fusarium graminearum. It contains many information for F. graminearum that the sequences, proteins, annotations, protein-protein interaction, pathogenic genes, sub-location of proteins, transcription factors and ortholog information with other fungi. 

 
 

NARROMI 

2013,1,Jan 

NARROMI is software for improving accuracy of GRNs inference. It is a novel method combining ordinary differential equation based recursive optimization (RO) and information-theory based mutual information (MI). In this algorithm, both noisy regulations with low pair-wise correlations and redundant regulations from indirect regulators are removed by measuring MI and implementing RO, respectively. In particular, it is the first one to handle the redundancy problem in GRNs for model based network inference methods. 

 

PCA-CMI 

2011,17,Nov 

PCA-CMI is a MATLAB program for inferring gene regulatory networks from gene expression data. It is a novel method based on path consistency algorithm and conditional mutual information, which consider the non-linear dependence and topological structure of GRNs. In this algorithm, the (conditional) dependence between a pair of genes is represented by the CMI between them. With the general hypothesis of Gaussian distribution underlying gene expression data, CMI between a pair of genes is computed by a concise formula involving the covariance matrices of the related gene expression profiles. 

 

DIPOS 

2011,29,Jun 

DIPOS provides a complete rice protein interaction database to the plant biologist to better understand the life of plants, especially for rice.

 

FPPI 

2009,9,May 

FPPI is a database about protein-protein interactions for Fussarium graminearum, which contain 223,116 interactions among 7,406 proteins and 27,102 interactions among 3,745 proteins in the core PPI set. Furthermore, this database contains various different functional information for F. graminearum genes.
 

CMI2NI

--- 

CMI2NI is a software for inferring gene regulatory networks from gene expression data. It is a novel method using a new proposed concept of Conditional Mutual Inclusive Information (CMI2) which can accurately measure direct dependences between genes. Given the small size samples of gene expression data, CMI2NI can not only infer the correct topology of the regulation networks but also accurately quantify the dependence or regulation strength between genes. CMI2NI provides a useful tool to infer gene regulatory networks.

在PLoS Computational Biology、Bioinformatics、Current Opinion in Biotechnology、Neucleic Acids Research、Plant Physiology、Journal of Proteome Research、Neural Networks和Pattern Recognition等国际著名学术期刊发表学术论文60余篇，其中SCI检索论文50余篇。参与编写专著3部。

代表性英文论文：

2015

1. Wang YY, Li Z, Chen T, Zhao XM. 2015. Understanding the aristolochic acid toxicities in rat kidneys with regulatory networks. IET Syst Biol, 9(4):141-6. doi: 10.1049/iet-syb.2014.0057.

2. Zhao XM. 2015. Guest Editorial. IET Syst Biol, 9(4):105. doi: 10.1049/iet-syb.2015.0057.

3. Dai YF, Zhao XM. 2015. A survey on the computational approaches to identify drug targets in the postgenomic era. Biomed Res Int, 2015:239654. doi: 10.1155/2015/239654. Epub 2015 Apr 28. Review.

4. Downs CA, Kreiner L, Zhao XM, Trac P, Johnson NM, Hansen JM, Brown LA, Helms MN. 2015. Oxidized glutathione (GSSG) inhibits epithelial sodium channel activity in primary alveolar epithelial cells. Am J Physiol Lung Cell Mol Physiol, 308(9):L943-52. doi: 10.1152/ajplung.00213.2014. Epub 2015 Feb 20.

5. Zhang X, Zhao J, Hao JK, Zhao XM, Chen L. 2015. Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks. Nucleic Acids Res, 43(5):e31. doi: 10.1093/nar/gku1315. Epub 2014 Dec 24.

6. Zhao XM, Liu KQ, Zhu G, He F, Duval B, Richer JM, Huang DS, Jiang CJ, Hao JK, Chen L. 2015. Identifying cancer-related microRNAs based on gene expression data. Bioinformatics, doi: 10.1093/bioinformatics/btu811. Epub 2014 Dec 12.

7. Wang HQ, Zheng CH, Zhao XM. 2015. jNMFMA: a joint non-negative matrix factorization meta-analysis of transcriptomics data. Bioinformatics, 31(4):572-80. doi: 10.1093/bioinformatics/btu679. Epub 2014 Oct 16.

2014

8. Wang M#, Zhao XM#, Tan H, Akutsu T, Whisstock J, and Song J. 2014. Cascleave 2.0, a new approach for predicting caspase and granzyme cleavage targets. Bioinformatics, 30(1): 71-80.

9. Chen WH, Zhao XM, Noort V and Bork P. 2014. Comments on "Human Dominant Disease Genes Are Enriched in Paralogs Originating from Whole Genome Duplication". PLoS Computational Biology, 10(7): e1003758.

10. Hong-Qiang Wang, Chun-Hou Zheng and Xing-Ming Zhao. 2014 jNMFMA: A joint non-negative matrix factorization meta-analysis of transcriptomics data. Bioinformatics, doi:10.1093/bioinformatics/btu679.

11. Xing-Ming Zhao, Luonan Chen 2014. Network-based biomarkers for complex diseases. Journal of Theoretical Biology. pii: S0022-5193(14)00400-7. doi: 10.1016/j.jtbi.2014.07.007.

12. Xing-Ming Zhao, Alioune Ngom, Jin-Kao Hao 2014. Pattern recognition in bioinformatics. Neurocomputing, 145:1-2.

13. Guimin Qin, Xing-Ming Zhao* 2014. A survey on computational approaches to identifying disease biomarkers based on molecular networks. Journal of theoretical biology, pii: S0022-5193(14)00344-0. doi: 10.1016/j.jtbi.2014.06.007.

14. Zhao XM, Gao JX, Nacher JC 2014. Data mining in translational bioinformatics. BioMed Research International. 2014: 656519.

15. Li S, Kang L, and Zhao XM* . 2014. A Survey on Evolutionary Algorithm Based Hybrid Intelligence in Bioinformatics. BioMed Research International 2014: 362738.

16. Zhao, XM. 2014. Editorial: Part 1: Network biology in translational bioinformatics and systems biology. IET Systems Biology 8(2): 23.

17. Zhao, XM. 2014. Editorial: Part 2: Network biology in translational bioinformatics and systems biology. IET Systems Biology 8(3): 75.

2013

18. H Wang , H Zheng, Azuaje, F, Xing-Ming Zhao. (2013). "Drug-Domain Interaction Networks in Myocardial Infarction". IEEE Transactions on, NanoBioscience, 12(3), 182-188.

19. Wei-Hua Chen, Xing-Ming Zhao, Vera van Noort and Peer Bork. "Human monogenic disease genes have frequently functionally redundant paralogs". PLoS Computational Biology, 2013, 9(5): e1003073. (Co-first author)

20. Xiaoping Liu, Xiao-Dong Zhang, Wei-Hua Tang, Luonan Chen and Xing-Ming Zhao*. "eFG: an electronic resource for Fusarium graminearum". Database, doi: 10.1093/database/bat042, 2013.

21. Xiaoping Liu, Rui Liu, Xing-Ming Zhao and Luonan Chen. Detecting early-warning signals of type 1 diabetes and its leading biomolecular networks by dynamical network biomarkers"[J]. BMC Medical Genomics, 2013, 6(2): 1-10.

22. Xing-Ming Zhao, Weidong Tian, Jiang R, Wan J. "Computational Systems Biology". The Scientific World Journal. 2013: 350358. (Editorial)

23. Xing-Ming Zhao. "Network Biology: Towards Systematic Understanding of Biological Systems". Current Bioinformatics. 2013, 8(1):35-36. (Editorial)

24. Chenglei Sun, Zheng-Zheng Shi, Xiaobo Zhou, Luonan Chen, Xing-Ming Zhao*."Prediction of S-Glutathionylation Sites Based on Protein Sequences". PLoS ONE, 2013, 8(2): e55512.

25. Xiujun Zhang, Keqin Liu, Zhi-Ping Liu, Béatrice Duval, Jean-Michel Richer, Xing-Ming Zhao*, Jin-Kao Hao and Luonan Chen. "NARROMI: a noise and redundancy reduction technique improves accuracy of gene regulatory network inference". Bioinformatics, 2013, 29(1):106-113.

26. Xiao-Dong Zhang, Xing-Ming Zhao*. "Computational approaches for identifying signaling pathways from molecular interaction networks", Current Bioinformatics, 2013, 8(1): 56-62.

2012

27. Jiao Wang, Fuyan Hu, Hua Cheng, Xing-Ming Zhao*, TieqiaoWen. "A systems biology approach to identifying the signaling network regulated by Rho-GDI-γ during neural stem cell differentiation", Molecular BioSystems, 2012, 8, 2856-2923.

28. Mingjun Wang, Xing-Ming Zhao, Haisong Xu, Yuan Li, Tatsuya Akutsu, Jiangning Song. "FunSAV: predicting the functional effect of single amino acid variants using a two-stage random forest model", PLoS ONE, 2012, 7(8):e43847.

29. Haiying Wang, Huiru Zheng, Francisco Azuaje, Xingming Zhao. "Drug-Target Network in Myocardial Infarction: a Structural Analysis", the 2012 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2012).

30. Ke-Qin Liu, Zhi-Ping Liu, Jin-Kao Hao, Luonan Chen, Xing-Ming Zhao*. "Identifying dysregulated pathways in cancers from pathway interaction networks", BMC Bioinformatics, 2012, 13: 126.

31. Xiaoping Liu, Zhi-Ping Liu, Xing-Ming Zhao*, Luonan Chen. "Identifying disease genes and module biomarkers by differential interactions", J Am Med Inform Assoc, 19: 241-248, 2012.

32. Yin-Ying Wang, Jose C.Nacher, Xing-Ming Zhao*. "Predicting drug targets based on protein domains", Mol Biosyst, 8(5):1528-1534, 2012.

33. Yin-Ying Wang, Ke-Jia Xu, Jiangning Song, Xing-Ming Zhao*. "Exploring drug combinations in genetic interaction network", BMC Bioinformatics, 13(Suppl 7): S7, 2012.

34. Xiujun Zhang, Xing-Ming Zhao, Kun He, Le Lu, Yongwei Cao, Jingdong Liu, Jin-Kao Hao, Zhi-Ping Liu, Luonan Chen. "Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information", Bioinformatics, 28(1):98-104, 2012. 

2011

35. Xing-Ming Zhao, Murat Iskar, Georg Zeller, Michael Kuhn, Vera van Noort, Peer Bork, "Prediction of drug combinations by integrating molecular and pharmacological data", PLoS Comput Biol, Dec; 7(12): e1002323, 2011.

36. Murat Iskar, Georg Zeller, Xing-Ming Zhao, Vera van Noort, Peer Bork, "Drug discovery in the age of systems biology: the rise of computational approaches for data integration", Curr Opin Biotechnol. Dec 5, 2011.

37. Achyut Sapkota, Xiaoping Liu, Xing-Ming Zhao*, Yongwei Cao, Jingdong Liu, Zhi-Ping Liu, Luonan Chen. "DIPOS: database of interacting proteins in Oryza sativa", Molecular BioSystems, 7(9):2615-2621, 2011. 

2010

38. Xiang Tang, Zhi-Yong Zhang, Wen-Juan Zhang, Xing-Ming Zhao, Xuan Li, Dong Zhang, Qiao-Quan Liu and Wei-Hua Tang, "Global Gene Profiling of Laser-Captured Pollen Mother Cells Indicates Molecular Pathways and Gene Subfamilies Involved in Rice Male Meiosis", Plant Physiology, 154:1855-1870, 2010.

39. Xiaoping Liu, Wei-Hua Tang, Xing-Ming Zhao*, Luonan Chen, "A network approach to predict pathogenic genes for Fusarium graminearum", PLoS ONE, 5(10): e13021, 2010.

40. Jun-Feng Xia, Xing-Ming Zhao, Jiangning Song, De-Shuang Huang, "APIS: accurate prediction of hot spots in protein interfaces by combining protrusion index with solvent accessibility", BMC Bioinformatics, 11:174, 2010.

41. Jun-Feng Xia, Xing-Ming Zhao, De-Shuang Huang, "Predicting protein-protein interactions from protein sequences using meta predictor", Amino Acids, 39(5):1595-9, 2010.

42. Zikai Wu, Xing-Ming Zhao*, Luonan Chen, "A Systems biology approach to identify effective cocktail drugs", BMC Systems Biology (OSB2009 special issue), 4(Suppl 2): S7, 2010.

43. Chenglei Sun, Xing-Ming Zhao*, Weihua Tang, Luonan Chen, "FGsub: Fusarium graminearum protein subcellular localization prediction from primary structures", BMC Systems Biology (OSB2009 special issue), 4(Suppl 2): S12, 2010.

44. Xing-Ming Zhao, Yiu-Ming Cheung, De-Shuang Huang. "Analysis of Gene Expression Data Using RPEM Algorithm in Normal Mixture Model with Dynamic Adjustment of Learning Rate",International Journal of Pattern Recognition and Artificial Intelligence, 24(4): 651-666, 2010.

45. Xing-Ming Zhao, Luonan Chen, and Kazuyuki Aihara, "A discriminative approach to identifying domain-domain interactions from protein-protein interactions", Proteins, 78:1243-1253, 2010.

46. Xing-Ming Zhao, Yixue Li, Ming Li, Luonan Chen, "Advances in genome informatics", Journal of Bioinformatics and Computational Biology, 8 Suppl 1: v-viii, 2010. 

2009

47. Xing-Ming Zhao, Xiao-Wei Zhang, Weihua Tang, Luonan Chen, "FPPI: Fusarium graminearum protein-protein interaction database", Journal of Proteome Research, 8 (10): 4714-4721, 2009.

48. Zikai Wu, Xing-Ming Zhao, Luonan Chen, "Identifying responsive functional modules from protein-protein interaction network", Mol Cells, 27(3):271-277, 2009.

49. Xing-Ming Zhao, Ruisheng Wang, Luonan Chen, and Kazuyuki Aihara, "Automatic modeling of signaling pathways by network flow model", J Bioinform Comput Biol, 7(2):309-322, 2009. 

2008

50. Xing-Ming Zhao , Ruisheng Wang, Luonan Chen, and Kazuyuki Aihara, "Uncovering signal transduction networks from high-throughput data by integer linear programming", Nucl. Acids Res, 36(9):e48, 2008.

51. Xing-Ming Zhao , Yong Wang, Luonan Chen, Kazuyuki Aihara, "Gene function prediction using labeled and unlabeled data", BMC Bioinformatics, 9:57, 2008 .

52. Xing-Ming Zhao , Xin Li, Luonan Chen, Kazuyuki Aihara, "Protein classification with imbalanced data", Proteins, 70(4):1125-1132, 2008.

53. Xing-Ming Zhao , Yong Wang, Luonan Chen, Kazuyuki Aihara, "Protein domain annotation with integration of heterogeneous information sources", Proteins, 72(1):461-473, 2008.

54. Xing-Ming Zhao , Luonan Chen and Kazuyuki Aihara, "Protein function prediction with high-throughput data", Amino Acids, 35(3): 517-530, 2008. 

2006

55. Xing-Ming Zhao , Ji-Xiang Du, Hong-Qiang Wang, Yunping Zhu, and Yixue Li, "A new technique for selecting features from protein sequences", International Journal of Pattern Recognition and Artificial Intelligence, 20(2): 271–283, 2006.

56. De-Shuang Huang, Xing-Ming Zhao , Guang-Bin Huang,Yiu-Ming Cheung, "Classifying protein sequences using hydropathy blocks", Pattern Recognition, 39: 2293 – 2300, 2006. 

2005

57. Xing-Ming Zhao , Yiu-Ming Cheung, and De-shuang Huang, "A novel approach to extracting features from motif content and protein composition for protein sequence classification",Neural Networks, 18(8): 1019-1028, 2005.

58. Xing-Ming Zhao , Yiu-Ming Cheung, De-Shuang Huang, "A novel Markov pairwise protein sequence alignment method for sequence comparison", Protein and Peptide Letters, 12(7):665-669, 2005.

荣誉奖励：

1、2010年，入选上海市青年科技启明星。

2、2013年，入选上海市浦江人才计划。

 

9月19日，应我院赵兴明教授的邀请，英国Ulster大学Haiying Wang教授来到嘉定校区，在智信馆403室作了题为“Dynamics and Controllability in Cell Cycle Specific Protein Interaction Networks”的学术报告，此报告也是计算机科学与技术系智信讲坛第45期。 

报告会上，Haiying Wang教授首先系统地介绍了了蛋白质相互作用网络动态组织规律的研究背景，接着介绍了以酵母细胞周期实验数据驱动的动态过程研究，以及在细胞周期生物过程中两类hub基因（data hub和party hub）起到的作用和各自特点，最后介绍了基于minimum dominating set方法所识别出的关键蛋白（critical protein）和多余蛋白（redundant protein）在蛋白质相互作用网络中的拓扑结构特点、基因组中的必要性、功能相似性和在网络中的恢复力。报告过程中，我院学生与Haiying Wang教授交流充分，气氛热烈。 

报告结束后，Haiying Wang教授与赵兴明教授讨论了具体的合作事宜。 

 

来源：同济大学计算机科学与技术系 2015-10-08

同济大学高等研究院赵兴明研究员应邀来我室作学术报告

10月23日，同济大学高等研究院研究员赵兴明应邀来我室进行学术交流并作了题为“Computational approaches to reverse engineering gene regulatory networks”的学术报告。赵兴明研究员为上海市青年科技启明星计划入选者、上海市浦江人才计划入选者。担任IEEE Senior Member、IEEE SMC Technical Committee on Systems Biology委员、IAPR TC20 委员、中国运筹学会计算系统生物学分会理事、中国细胞学会基因组信息学与系统生物学分会理事和上海市生物信息学学会理事。同时担任了Journal of Theoretical Biology和BioMed Research International 等国际杂志的客座编辑和编委。已完成了863在内的多项科研课题，发表SCI检索论文50余篇。

来源：中国科学院海洋环流与波动重点实验室 2014-10-24 

医学系统生物学学组在我校召开成立会议

为促进计算系统生物学与医学的交叉融合，特别是在转化医学研究中的应用。5月11日—12日，中国运筹学会计算系统生物学分会在我校召开医学系统生物学学组成立会议。

会议由医学系统生物学学组组长沈百荣教授主持,陈洛南教授代表计算系统生物学分会对医学系统生物学学组成立表示祝贺并做特邀报告。张世华 (中国科学院数学与系统科学研究院)；戴晓峰 (江南大学生物工程学院)；高琳(西安电子科技大学计算机学院)；刘雷 (复旦大学生物医学研究院)；薛宇 (华中科技大学生命科学与技术学院)；王海芸(同济大学生命科学院)；王侃侃 (上海交通大学医学院附属瑞金医院)；王勇 (中国科学院数学与系统科学研究院)；王进(南京大学生命科学院);吴凌云(中国科学院数学与系统科学研究院)；赵兴明(同济大学电子与信息工程学院)等12位专家结合自己的研究工作做了丰富精彩的专题报告，讨论了大数据时代的系统生物学与其它学科交叉的迫切性和学组成立、发展的重要性。

学术会议结束后，评弹学校青年艺术家孙可老师介绍了苏州评弹艺术并作了精彩表演，大家饶有兴致的讨论了科学与艺术结合对创新思维促进的系统生物学基础。会议确定10月份在青岛举行医学系统生物学学组第一次workshop。

来源：苏州大学国家技术转移中心 2014-05-15