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  • 简要介绍:研究员,博士生导师。香港城市大学博士, 日本京都大学博士后, 美国伊利诺伊大学香槟分校访问学者(2013-2014),日本京都大学访问副教授(2016)。UniProt 国际科学顾问委员会委员,中国计算机学会生物信息专业委员会创始委员,中国人工智能学会生物信息与人工生命专业委员会创始委员、中国中文信息处理学会医疗健康与生物信息处理专业委员会创始委员,中国细胞生物学会生物信息与系统生物学分会理事,中国运筹学会计算系统生物学分会理事。主持或完成四项国家自然科学基金项目,以及多个国内外企业研发项目。主要研究方向为人工智能与生物医学大数据挖掘,特别是生物医学文本挖掘、蛋白功能预测、宏基因组、药物发现、免疫信息学等。相关论文在生物信息、人工智能、数据挖掘等顶级国际会议和期刊发表,如 NeurIPS, KDD, ISMB, IJCAI, IEEE Transaction on Cybernetics, Bioinformatics, Nucleic Acids Research等。2014年-2020 年参加 BioASQ 大规模生物医学文本自动标注国际竞赛中取得六次第一名的好成绩。2017 年参加 CAFA 大规模蛋白功能自动标注国际竞赛,在全世界50多个实验室中获得第一名。指导硕士生张连明获得2014年上海市研究生优秀成果(学位论文),博士生高骏宁获得2018年IEEE生物信息学和生物医学国际会议(BIBM2018)最佳学生论文。
  • 代表成果:

    1.Ronghui You, Shuwei Yao, Hiroshi Mamitsuka, Shanfeng Zhu*, DeepGraphGO: graph neural network for large-scale, multispecies protein function prediction. ISMB/ECCB2021, In Press

    2.Shuwei Yao, Ronghui You, Shaojun Wang, Yi Xiong, Xiaodi Huang, Shanfeng Zhu*. NetGO 2.0: improving large-scale protein function prediction with massive sequence, text, domain, family and network information. Nucleic Acids Res. In press,

    3.Junyi Bian, Li Huang, Xiaodi Huang, Hong Zhou and Shanfeng Zhu*.GrantRel: Grant Information Extraction via Joint Entity and Relation Extraction. Findings of ACL2021, In Press

    4.Ronghui You, Yuxuan Liu, Hiroshi Mamitsuka, Shanfeng Zhu*. BERTMeSH: deep contextual representation learning for large-scale high-performance MeSH indexing with full text. Bioinformatics, 37(5): 684-692 (2021)

    5.Suyang Dai, Ronghui You, Zhiyong Lu, Xiaodi Huang, Hiroshi Mamitsuka, Shanfeng Zhu*: FullMeSH: improving large-scale MeSH indexing with full text. Bioinformatics. 36(5): 1533-1541(2020)

    6.Ronghui You, Zihan Zhang, Ziye Wang, Suyang Dai, Hiroshi Mamitsuka, Shanfeng Zhu*: AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification. NeurIPS 2019: 5812-5822(2019)(First Place of BioASQ 8a and 7a)

    7.Shengwen Peng, Ronghi You, Hongning Wang, Hiroshi Mamitsuka, Shanfeng Zhu*, DeepMeSH: Deep Semantic Representation for Improving Large-scale MeSH Indexing. Bioinformatics(ISMB2016), 2016,32(12) i70-79(First Place of BioASQ 5a and 4a)

    8.Ke Liu, Shengwen Peng, Junqiu Wu, Chengxiang Zhai, Hiroshi Mamitsuka, Shanfeng Zhu*, MeSHLabeler: Improving the Accuracy of large-scale MeSH indexing by Integrating Diverse Evidence. Bioinformatics(ISMB2015), 2015, 31(12): i339-i347(First Place of BioASQ 3a and 2a)

    9.Junning Gao, Makoto Yamada, Samuel Kaski, Hiroshi Mamitsuka, Shanfeng Zhu*, A Robust Convex Formulation for Ensemble Clustering. Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI 2016),New York,USA Jul. 2015,AAAI Press,1476-1482

    10.Xiaodong Zheng, Shanfeng Zhu*, Junning Gao, Hiroshi Mamitsuka, Instance-wise Weighted Nonnegative Matrix Factorization for Aggregating Partitions with Locally Reliable Clusters. Proceedings of the 24th International Joint Conference on Artificial Intelligence(IJCAI 2015),Buenos Aires, Argentina Jul. 2015, AAAI Press,4091-4097.

    11.Lizhi Liu, Xiaodi Huang, Hiroshi Mamitsuka, Shanfeng Zhu*, HPOLabeler: Improving prediction of human protein-phenotype associations by learning to rank. Bioinformatics 36(14)4180-4188(2020)

    12.Ronghui You, Shuwei Yao, Yi Xiong, Xiaodi Huang, Fengzhu Sun, Hiroshi Mamituska, Shanfeng Zhu*, NetGO: improving large-scale protein function prediction with massive network information. Nucleic Acids Research,47(W1),W379–W387(2019)

    13.Ronghui You, Zhihan Zhang, Yi Xiong, Fengzhu Sun, Hiroshi Mamitsuka, Shanfeng Zhu* GOLabeler: Improving Sequence-based Large-scale Protein Function Prediction by Learning to Rank. Bioinformatics 34(14)2465-2473,(2018) (First Place in CAFA3)

    14.Ziye Wang, Zhengyang Wang, Yang Young Lu, Fengzhu Sun*, Shanfeng Zhu* SolidBin: improving metagenome binning with semi-supervised normalized cut. Bioinformatics 35(21): 4229-4238(2019)

    15.Qingjun Yuan, Junning Gao, Dongliang Wu, Shihua Zhang, Hiroshi Mamitsuka, Shanfeng Zhu*, DrugE-Rank: improving drug–target interaction prediction of new candidate drugs or targets by ensemble learning to rank. Bioinformatics(ISMB2016),2016 32(12). i18-27