个人简介：

刘琦，天游线路检测中心生物信息系长聘教授，博士生导师，天游线路检测中心上海自主智能无人系统科学中心PI。国家级人才。长期致力于发展人工智能技术赋能的组学解析和精准干预，进行数据驱动的精准医学研究和转化（“AI for Precision Medicine”）。近年来发展了一系列面向组学数据多尺度（单细胞组学），跨模态（多模态组学），有扰动（扰动组学）等特点的AI智能解析的计算方法和计算模型（Nature Computational Science 2024a, 2024b; Cell Genomics 2024; Science Advances 2020; Nature Communications 2019; Genome Biology 2024, 2022; Nucleic Acids Research 2021; Science China – Life Science 2024, 2022;  MICCAI 2024），并基于组学智能解析形成面向重大疾病（如肿瘤）的精准干预：包括精准药物诊疗（Nature Communications 2021，2015；Genome Medicine 2023; Science Bulletins 2022a；Chemical Science 2020），精准免疫治疗（Nature Machine Intelligence 2023a, 2023b; Genome Medicine 2019），以及精准基因编辑（Nature Communications 2023; Genome Biology 2018; Science Bulletins 2022b; Nucleic Acids Research 2020）。受邀在Trends 和WIREs系列（Trends Mol. Med. 2019; Trends Pharmacol. Sci. 2017; Trends Biotechnol. 2016, WIREs Comput. Mol. Sci. 2018）以及计算机科学领域高影响力的期刊和会议如IEEE TKDE/MICCAI/SDM/ICDM等发表论文。其成果先后被Nature Machine Intelligence进行Research Highlight,Cell Genomics Featured Article, 2次入选中国生物信息学算法十大进展，获F1000推荐，入选Trends系列年度“Best of Trends”Award。著《组学机器学习》（科学出版社，2023）。授权精准干预相关发明专利4项（2项组合用药，1项新抗原，1项基因编辑），和罗氏合作申请PCT专利1项（基因编辑）。任ELSEVIER出版社人工智能生命科学交叉领域期刊Artificial Intelligence in the Life Sciences创刊编委，华为公司科学顾问。曾入选《麻省理工科技评论》中国智能计算创新人物、获药明康德生命化学研究奖、吴文俊人工智能自然科学技术奖、微众学者奖、华夏医学科技奖、上海生物信息学会“青年卓越奖”等。入选上海市浦江人才、上海市科技启明星人才、上海市曙光人才、上海市优秀学术带头人。

目前主持及参与了科技部BT&IT重大专项、精准医学重点研发计划、慢病专项重点研发计划，科技部863计划生物信息学重大专项，国家自然科学基金委生命语言AI重点专项，上海市基础特区项目，上海市计算生物学重点专项等多项国家和省部级项目。和国际制药公司及互联网公司开展了广泛的合作。同时承担学院本科生“机器学习理论与方法”（上海市一流课程、上海市重点课程建设、智慧树慕课）以及“生物信息学算法与实践”的专业必修课教学任务, 积极进行生物信息学、药物研发和人工智能方向的科普宣传（见: 生物信息学研究的思考，化学界诞生了一个AlphaGO,人工智能应用于新药研发的范式转变，联邦学习能否打破新药研发的反摩尔定律）,开展双语及全英文课程建设。于2018年-2023年共5年作为领队教练带领天游线路检测中心本科生团队获得国际合成生物学大赛（iGEM）金奖，并于2021年获得iGEM 软件赛道全球Best Software & AI Project奖（Village Awards）。

编写著作：

u组学机器学习 科学出版社 2023（独著）

u可解释人工智能导论 电子工业出版社 2022 （参编）

（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）

u人工智能与药物设计 化学工业出版社 2023 （参编）

近年代表性论文：

[1]. Qi Liu*, Translating “AI for omics” into precision therapy, Medicine Plus, 2024.

[2]. Yicheng Gao et al, Qi Liu*, Toward subtask decomposition-based learning and benchmarking for genetic perturbation outcome prediction and beyond, Nature Computational Science 2024.

[3]. Yicheng Gao, Qi Liu*, Delineating the cell types with transcriptional kinetics, Nature Computational Science 2024.

[4].  Liangzi Mengfang et al, Qi Liu*, Genomics-guided Representation Learning for Pathologic Pan-cancer Tumor Microenvironment Subtype Prediction, MICCAI 2024.

[5]. Yicheng Gao et al., Qi Liu*, Unified cross-modality integration and analysis of T-cell receptors and T-cell transcriptomes by low-resource-aware representation learning, Cell Genomics. Advance Access, 2024. (Cell Genomics Featured Article!)

[6]. Fengying Sun et al., Qi Liu*… ShiTie Liu*, Single- Cell Omics: experimental workflow, data analyses and applications, Science China - Life Sciences. Advance Access, 2024.

[7]. Bin Duan et al., Qi Liu*, Multi-slice Spatial Transcriptome Domain Analysis with SpaDo, Genome Biology. Advance Access, 2024.

[8]. Chen Tang et al., Qi Liu*, Personalized tumor combination therapy optimization using the single-cell transcriptome, Genome Medicine. Advance Access, 2023.

[9]. Qichang Chen et al., Qi Liu*, Genome-wide CRISPR off-target prediction and optimization using RNA-DNA interaction fingerprints, Nature Communications. Advance Access, 2023.

[10]. Yichen Gao et al., Qi Liu*, Pan-Peptide Meta Learning for T-Cell Receptor-Antigen Binding Recognition, Nature Machine Intelligence. Advance Access, 2023. （Nature Machine Intelligence Research Highlight! ESI高引）

[11]. 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.

[12]. Qinchang Chen et al, Qi Liu*, Toward a molecular mechanism-based prediction of CRISPR-Cas9 targeting effects, Science Bulletin, Advance Access, 2022.

[13]. Dongyu Xue et al, Qi Liu*, X-MOL: large-scale pre-training for molecular understanding and diverse molecular analysis, Science Bulletin, Advance Access, 2022.

[14]. 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.

[15]. Yukong Gong et al, Qi Liu*, DeepReac+: Deep active learning for quantitative modeling of organic chemical reactions, Chemical Science, Advance Access, 2021.

[16]. Biyuzhang et al, Qi Liu*, The tumor therapy landscape of synthetic lethality, Nature Communications, Advance Access, 2021.

[17]. Bin Duan et al, Qi Liu*, Integrating multiple references for single cell assignment, Nucleic Acids Research, Advance Access, 2021.

[18]. Bin Duan et al, Qi Liu*, Learning for single cell assignment, Science Advances, Advance Access, 2020. (入选2020年中国生物信息学算法十大进展) 

[19].Jifang Yan et al, Qi Liu*, Benchmarking and integrating CRISPR off-target detection and prediction, Nucleic Acids Research, Advance Access, 2020.

[20]. Chi Zhou et al, Qi Liu*, pTuneos: prioritizing Tumor neoantigens from next-generation sequencing data, Genome Medicine, Advance Access, 2019.   

[21]. 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 )

[22]. Bin Duan et al, Qi Liu*, Model based Understanding of Single-cell CRISPR Screening, Nature Communications, Advance Access, 2019. (入选2019年中国生物信息学算法十大进展)

[23]. 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.

[24]. Guohui Chuai et al, Qi Liu*, DeepCRISPR: optimized CRISPR guide RNA design by deep learning, Genome Biology, Advance Access, 2018.  (F1000 Recommendation)

[25]. 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!) 

[26]. 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!)

[27]. 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.

课题组常年招聘助理教授，博士后，研究助理；并欢迎博士生，硕士生，本科生报考和交流。