曙光计划

刘琦

L

发布时间:2025-04-28  

       

姓         名:刘琦

学         位:博士

导师情况:博士生导师

研究领域:生物信息学(组学人工智能)

E-mail:qiliu@tongji.edu.cn

通讯地址:上海市四平路1239号,同济大学生命科学与技术学院(200092)

实验室主页:中文:http://bm2.tongji.edu.cn/

            英文:https://ai4omics.github.io/


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 WIREs系列等发表综述。其成果获相应期刊Research HighlightF1000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。2018-2024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 ProjectVillage Awards


编写著作:

u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 MedicineAdvance 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.

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