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个人简介 Personal Profile         刘琦, 浙江大学生物信息学博士, 美国佐治亚大学系统生物学博士联合培养, 香港科技大学人工智能方向博士后。现任同济大学生物信息系长聘教授, 博士生导师，系中国计算机学会（CCF）杰出会员。上海市曙光人才，上海市启明星人才，上海市浦江人才，上海市优秀学术带头人。任ELSEVIER出版社人工智能生命科学交叉领域杂志 Artificial Intelligence in the Life Sciences编委。其在人工智能和生物组学交叉领域的研究工作先后多次入选中国生物信息学研究十大进展，获F1000推荐。获吴文俊人工智能自然科学技术奖、药明康德生命化学奖、微众学者奖等。入选教育部“青年长江学者”。        刘琦教授的主要研究方向为人工智能和生物信息学。我们的生物医药大数据挖掘课题组(BM2, stand for Biological and Medical Big Data Mining) 致力于“AI for Omics” 交叉研究。发展和应用人工智能技术，结合生物组学分析（如单细胞组学，药物基因组学，免疫组学，CRISPR功能基因组学等），进行复杂疾病（肿瘤）的精准药物治疗（靶点识别、药物发现、精准用药、免疫治疗）和精准基因治疗（基因编辑）等精准医学研究。具体包括： （1）基于人工智能，发展单细胞组学，药物基因组学、免疫组学, 功能基因组学等组学数据分析的新理论、新模型和新算法，提高公共生物组学分析的再利用价值。（2）基于人工智能和生物组学挖掘，进行复杂疾病（肿瘤）的靶点识别，药物发现，精准用药以及精准免疫治疗等领域的应用和转化研究。（3）基于人工智能和生物组学挖掘，进行CRISPR基因编辑系统的优化设计和精准基因治疗等领域的应用和转化研究。        近年来课题组逐步形成了人工智能和组学数据分析相结合的"AI for Omics"交叉融合的研究范式，在领域内具备一定的特色，我们希望以该类研究范式为基础，紧密的和临床实验科学家合作，解决复杂疾病的精准诊断和治疗难题。课题组已开发了一系列的生物信息学及药物信息学分析软件平台并形成专利, 承担及参与了国家及省部级科研项目, 并且与相关医院, 制药企业及数据挖掘工业界有紧密的合作和联系。        承担本科生“机器学习理论与方法”（课程链接）及“生物信息学算法与实践”课程的教学和课程建设工作。依托智慧树慕课平台，进行面向生物医学专业方向的机器学习线上线下课程建设 （智慧树平台网上课程链接）。      编写著作：《可解释人工智能导论》（京东链接）（杨强，范力欣，朱军，陈一昕，张拳石，朱松纯，陶大程，崔鹏，周少华，刘琦，黄萱菁，张永峰）报告视频：生物医药数据的度量、嵌入、迁移和联邦（B站链接）公众号访谈：AI+时代 科研加速生物医药研发进程 （链接） 研究方向Research Directions 生物信息学,人工智能 2. 机电结构优化与控制 研究内容：在对机电结构进行分析和优化的基础上，运用控制理论进行结构参数的调整，使结构性能满足设计要求。1. 仿生结构材料拓扑优化设计, 仿生机械设计 研究内容：以仿生结构为研究对象，运用连续体结构拓扑优化设计理论和方法，对多相仿生结构(机构)材料进行2. 机电结构优化与控制 研究内容：在对机电结构进行分析和优化的基础上，运用控制理论进行结构参数的调整，使结构性能满足设计要求。1. 仿生结构材料拓扑优化设计, 仿生机械设计 研究内容：以仿生结构为研究对象，运用连续体结构拓扑优化设计理论和方法，对多相仿生结构(机构)材料进行整体布局设计。 整体布局设计。 报考意向 招生信息 生命科学与技术学院 硕士研究生 序号 专业 招生人数 年份 1 生物学 2 2023 2 生物学 2 2024 博士研究生 序号 专业 招生人数 年份 1 生物学 1 2023 2 生物学 2 2024 博士1： 生物信息学博士2： 生物信息学 报考意向 姓名: 手机号码： 邮箱： 毕业院校： 所学专业： 报考类型： 博士 硕士 个人简历*： 上传附件 支持扩展名：.rar .zip .doc .docx .pdf .jpg .png .jpeg 成绩单*： 上传附件 支持扩展名：.rar .zip .doc .docx .pdf .jpg .png .jpeg 其他材料： 上传附件 支持扩展名：.rar .zip .doc .docx .pdf .jpg .png .jpeg 备注： 提交 研究成果 [1].Xiaowen Wang et al, Qi Liu#, Qin Liu#, A complete graph-based approach with multi-task learning for predicting synergistic drug combinations, Bioinformatics, Advance Access, 2023.[2].Yaokai Nan et al, Qi Liu#,Qin Liu#, Semi-supervised heterogeneous graph contrastive learning for drug-target interaction perdicition, Computers in Biology and Medicine, Advance Acess, 2023[3]. Yicheng Gao et al., Qi Liu#, Pan-Peptide Meta Learning for T-Cell Receptor-Antigen Binding Recognition, Nature Machine Intelligence. Advance Access, 2023.[4]. Qinhu Zhang et al., Qi Liu#, Deshuang Huang#, Computational prediction and characterization of cell-type-specific and shared binding sites, Bioinformatics. Advance Access, 2023.[5].Gongchen Zhang et al., Qi Liu#, Systematic exploration of optimized base-editing gRNA design and pleiotropic effects with BExplorer, Genomics Proteomics & Bioinformatics. Advance Access, 2022.[6]. Shaoqi Chen et al, Qi Liu#, Privacy-preserving integration of multiple institutional data for single-cell type identification with scPrivacy, Science China-Life Sciences, Advance Access, 2022.[7]. Zhiting Wei et al.,Qi Liu#, DrSim: Similarity learning for transcriptional phenotypic drug discovery, Genomics Proteomics & Bioinformatics. Advance Access, 2022.[8]. Qinchang Chen et al, Qi Liu#, Toward a molecular mechanism-based prediction of CRISPR-Cas9 targeting effects, Science Bulletin, Advance Access, 2022.[9]. Qinhu Zhang et al, Qi Liu#, Deshuang Huang#, Base-resulotion prediction of transcription  factor binding signals by a deep learning framework, Plos Computational Biology, Advance Access, 2022.[10]. Dongyu Xue et al, Qi Liu#, X-MOL: large-scale pre-training for molecular understanding and diverse molecular analysis, Science Bulletin, Advance Access, 2022.[11].Gaoyang Li et al, Qi Liu#, A deep generative model for multi-view profiling of single cell RNA-seq and ATAC-seq data, Genome Biology, Advance Access, 2022.[12]. Yukang Gong et al, Qi Liu#, DeepReac+: Deep active learning for quantitative modeling of organic chemical reactions, Chemical Science, Advance Access, 2021.[13]. Xiaowen Wang, Qi Liu#, Qin Liu#, PRODeepSyn: integrating protein-protein interaction network with omics data to predict anticancer synergistic drug combinations, Briefings in Bioinformatics, Advance Access, 2021.[14]. Tengbo Zhang et al, Qi Liu#, Ping Wang#, iCRISEE: an integrative  analysis of CRISPR screen by reducing false positive hits, Briefings in Bioinformatics, Advance Access, 2021.[15]. Biyu Zhang et al, Qi Liu#, The tumor therapy landscape of synthetic lethality, Nature Communications, Advance Access, 2021.[16]. Bin Duan et al, Qi Liu#, Integrating multiple references for single cell assignment, Nucleic Acids Research, Advance Access, 2021.[17]. Qinhu Zhang et al, Qi Liu#, Deshuang Huang#, Locating transcription factor binding sites by Fully Convolutional Neural Network, Briefings in Bioinformatics, Advance Access, 2021.[18] . Xiangyong Li et al, Qi Liu#, Hao Ye#, Benchmark HLA genetyping and clarifying HLA impact on survival in tumor immunotherapy,  Molecular Oncology, Advance Access, 2021.[19]. Bin Duan et al, Qi Liu#, Learning for single cell assignment, Science Advances, Advance Access, 2020. (入选2020年中国生物信息学应用十大进展 )[20]. Jifang Yan et al, Qi Liu#, Benchmarking and integrating CRISPR off-target detection and prediction, Nucleic Acids Research, Advance Access, 2020.[21]. Shaoqi Chen et al, Qi Liu#, FL-QSAR: a federated learning based QSAR prototype for collaborative drug discovery, Bioinformatics, Advance Access, 2020.[22]. Zhiting Wei et al, Qi Liu#, iDMer: an integrative and Mechanism-driven response system for identifying compound interventions for sudden virus break, Briefings in Bioinformatics, Advance Access, 2020.[23]. Zhiting Wei et al, Qi Liu#, The landscape of tumor fusion neoantigens: a pan-cancer analysis, iScience, Advance Access, 2019. [24]. Chi Zhou et al, Qi Liu#, pTuneos: prioritizing Tumor neoantigens from next-generation sequencing data, Genome Medicine, Advance Access, 2019. [25]. Han Zhao et al, Qi Liu#, MetaMed: Linking microbiota functions with medicine therapeutics, mSystems, Advance Access, 2019. [26]. Chi Zhou et al, Qi Liu#, Towards in silico identification of tumor neoantigens in immunotherapy, Trends in Molecular Medicine, Advance Access, 2019. (Selected as one of the Best Review Article in Cell Trends 2019! Report Link )[27]. Yuli Gao et al, Qi Liu#, Data Imbalance in CRISPR off-target prediction, Briefings in Bioinformatics, Advance Access, 2019. [28]. Bin Duan et al, Qi Liu#, Model based Understanding of Single-cell CRISPR Screening, Nature Communications, Advance Access, 2019. (入选2019年中国生物信息学应用十大进展 )[29]. Chenyu Zhu et al, Qi Liu#, C3: Consensus Cancer Driver Gene Caller, Genomics, Proteomics & Bioinformatics, Advance Access, 2019. [30]. Dongyu Xue et al, Qi Liu#, Advances and challenges in deep generative models for de novo molecule generation, WIREs Computational Molecule Science, Advance Access, 2018. [31]. Guohui Chuai et al, Qi Liu#, DeepCRISPR: optimized CRISPR guide RNA design by deep learning, Genome Biology, Advance Access, 2018.        (F1000 Recommendation)[32]. Ke Chen et al, Qi Liu#, Towards in-silico prediction of the immune-checkpoint blockade response, Trends in Pharmacological Sciences, Advance Access, 2017. (Most read article in the latest 30 days after publication!) [33]. Jifang Yan et al, Qi Liu#, Benchmarking CRISPR on-target sgRNA design, Briefings in Bioinformatics, Advance Access, 2017. [34]. Jifang Yan et al, Qi Liu#, Metatopics: an integration tool to analyze microbial community profile by topic model,  BMC Genomics, Advance Access, 2017. [35]. Guohui Chuai, Jifang Yan et al, Qi Liu#, Deciphering relationship between microhomology and in-frame mutation occurence in human CRISPR-based gene knockout, Molecular Therapy-Nucleic Acids, Advance Access, 2016. (Featured Article ! )[36].  Jian Ma et al, Qi Liu#, Han Xu# and X. Shirley Liu#, CRISPR-DO: A genome-wide CRISPR designer and optimizer for multiple species, Bioinformatics, Advance Access, 2016. [37]. Guo-hui Chuai, Qi-Long Wang, Qi Liu#, In-silico meets in-vivo: towards computational CRISPR-based sgRNA design, Trends in Biotechnology, Advance Access, 2016. (Most read article in the latest 30 days after publication!)[38]. Haiping Wang, Quanquan Gu,Jia Wei, Zhiwei Cao, Qi Liu#, Mining drug-disease relationships as a complement to medical genetics-based drug repositioning: Where a recommendation system meets GWAS, Clinical Pharmacology & Therapeutics, Advance Access, 2015. [39]. Yi Sun, Zhen Sheng, Chao Ma, Kailin Tang, Ruixin Zhu, Zhuanbin Wu, Ruling Shen, Jun Feng, Dingfeng Wu, Danyi Huang, Dandan Huang, Jian Fei#, Qi Liu#, Zhiwei Cao#, Combining genomic and network characteristics for extended capability in predicting synergistic drugs for cancer, Nature Communications, Advance Access, 2015.[40]. WZ, LJ, KT, HP W, RX Z, WJ, ZW, Qi Liu#, When drug discovery meets web searching: learning to rank for ligand-based virtual screening, Journal of Cheminformatics, Advance Access, 2015. [41]. Haoqi Sun, Haiping Wang, Ruixin Zhu, Kailin Tang, Qin Gong, Juan Cui, Zhiwei Cao, Qi Liu#, iPEAP: integrating multiple omics and genetic data for pathway enrichment analysis, Bioinformatics, Advance Access online, 2013.[42]. Yi Sun, Ruixin Zhu, Hao Ye, Jing Zhao, Yujia Chen, Qi Liu# and Zhiwei Cao#, Towards a bioinformatics analysis of anti-Alzheimer’s herbal medicines from a target network perspective, Briefings in Bioinformatics, May;14(3):327-43, 2012.[43]. Qi Liu, Han Zhou, Ruixin Zhu, Ying Xu and Zhiwei Cao, Reconsideration of in-silico siRNA design from a perspective of heterogeneous data integration: problems and solutions, Briefings in Bioinformatics, Advance Access, 2012.[44]. Hong Kang, Zhen Sheng, Ruixin Zhu, Qi Huang, Qi Liu# and Zhiwei Cao#, A virtual drug screen schema based on multi-view similarity integration and ranking aggregation, 26;52(3):834-43, J. Chem. Inf. Model. 2012.[45]. Qi Liu, V. Olman, Huiqing Liu, Xiuzi Ye, Shilun Qiu, Ying Xu, RNACluster : an integrated tool for RNA secondary structure comparison and clustering, Journal of Computational Chemistry, 26(9), 1517-1526, 2008. 学生信息 研究生 程小桔 高溢骋 学生信息 当前位置：教师主页 > 学生信息 入学日期 所学专业 学号 学位 招生信息 当前位置：教师主页 > 招生信息 招生学院 招生专业 研究方向 招生人数 推免人数 考试方式 招生类别 招生年份