个人简介：

刘琦，同济大学生命科学与技术学院生物信息系长聘特聘教授，博士生导师，同济大学上海自主智能无人系统科学中心PI。国家杰出青年科学基金项目获得者，教育部长江学者青年学者。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/AI4PM”）。在智能计算方法学领域代表性期刊如Nature Methods, Nature Machine Intelligence, Nature Computational Science等及计算机科学领域重要期刊和会议如IEEE TKDE/SDM/ICDM/MICCAI等发表论文，受邀在Trends 和WIREs系列等发表综述。其成果获相应期刊Research Highlight、F1000推荐、ESI高引、中国生物信息学算法十大进展等。著《组学机器学习》（科学出版社，2023）。中国计算机学会杰出会员。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奖、微众学者奖，华夏医学科技奖等。 

曾经或者正在主持及参与科技部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-2024年连续6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）

代表性论文：

2025年：

[1]. Shaliu Fu et al, Qi Liu*, Benchmarking single-cell multi-modal data integrations, Nature Methods, Advance Access, 2025.

[2]. Yuli Gao et al, Qi Liu*, Weakly-supervised peptide-TCR binding prediction facilitates neoantigen identification, Cell System, Advance Access. 2025.

[3]. Yichen Gao et al, Qi Liu*, Causal disentanglement for single-cell representations and controllable counterfactual generation, Nature Communications, Advance Access, 2025.

[4]. Dongdong Mao et al, Qi Liu*, Gonghui Li*, Heterogeneous driving effect guides personalized tumor treatment targeting N6-methylatedadenine, Cancer Research, Advance Access, 2025.

[5]. Xiaojie Chen et al, Qi Liu*, SpaLinker identifies phenotype-associated spatial tumor microenvironment features by integrating bulk and spatial sequencing data, Cell Genomics, Advances Access, 2025.

[6]. Hao Xu et al, Qi Liu*, Jing-Dong Jackie Han*, Biomedical data and AI, Science China-Life Science, Advance Access, 2025.

[7]. Pengxiang Wang et al, Qi Liu*, Jia Fan*, Xinrong Yang*, Exploring Morphological Heterogeneity of Circulating Tumor Cells: Machine Learning-Based Approach for Cell Identification and Prognostic Implications, Science Bulletin, Advance Access, 2025.

2024年：

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

[2]. Wenhui Li et al, Qi Liu*, Discovering CRISPR-Cas system with self-processing pre-crRNA capability by foundation models, Nature Communications, Advance Access, 2024.

[3]. Zhiting Wei et al, Qi Liu*, PerturBase: a comprehensive database for single-cell perturbation data analysis and visualization, Nucleic Acids Research, Advance Access, 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, Advance Access, 2024. （Nature Computational Science Research Highlight!）

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

2023年：

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

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

[3]. 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）

2022年：

[1]. 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.

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

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

[4]. 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.

2021年：

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

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

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

2020年：

[1]. 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) 

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

2019年：

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

[2]. 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)

[3]. 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 )

2019年之前：

[1]. 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.

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

[3]. 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!) 

[4]. 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!)

[5]. 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。