个人简介：

刘琦，同济大学生物信息系长聘教授，博士生导师，同济大学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 2024; 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杰出青年基金，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同济大学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]. Wenhui Li et al, Qi Liu*, Discovering CRISPR-Cas system with self-processing pre-crRNA capability by foundation models, Nature Communications 2024.

[3].Zhiting Wei et al, Qi Liu*, PerturBase: a comprehensive database for single-cell perturbation data analysis and visualization, Nucleic Acids Research 2024.

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

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

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

[7]. 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!)

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

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

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

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

[12]. 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）

[13]. 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.

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

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

[16]. 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.

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

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

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

[20]. 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) 

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

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

[23]. 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 )

[24]. 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)

[25]. 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.

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

[27]. 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!) 

[28]. 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!)

[29]. 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。