人才队伍

1988年获北京大学遗传学学士；1991年毕获中国科学院发育生物学研究所发育生物学硕士；1997年获美国康涅狄格大学细胞生物学博士；1997至1999年在美国康涅狄格大学计算机科学与工程系从事博士后研究。曾任美国伊利诺伊大学（University of Illinois at Urbana-Champaign）生物技术中心生物信息学实验室主任（Director of Bioinformatics）、美国伊利诺伊大学动物学系助理教授、美国伊利诺伊大学国家超级计算中心(National Center for Supercomputing Application, NCSA)研究员（Faculty Fellow）。回国后，曾任中国科学院上海生命科学院“百人计划”研究员、复旦大学（附属）上海市公共卫生临床中心转化医学部主任、上海生物信息技术研究中心副主任、复旦大学大数据研究院医学信息与医学影像智能诊断研究所所长等职。

现任复旦大学智能医学研究院（筹）常务副院长，国际健康科学信息学研究院（IAHSI）院士，中国研究型医院学会临床数据与样本资源库专业委员会副主任委员、中华医学会医学信息分会常务委员等。

Email:liulei@fudan.edu.cn

现在承担和参与的课题：

主持：

1.上海市“地高建”项目，复旦大学上海医学院医学科研数据中心，项目编号：DGF501010；年度：2020-持续，项目负责人，经费：2879万

2.国家重点研发计划“精准医学研究”重点专项“疾病研究精准医学知识库构建”项目，项目编号：2016YFC09011900；年度：2016-2020，项目首席，经费：4632万。

参与：

1.国家自然科学基金重大研究计划“大数据驱动的管理与决策研究”2018年度集成项目“真实世界大数据驱动的全景式健康医疗管理和服务模式研究”，项目编号91846302；年度：2019-2022，参与，经费：700万

2.上海市经信委软件和集成电路产业发展专项资金项目“基于认知计算的智能医疗云服务研发及产业化应用”项目，年度：2017-2020，参与，经费：150万。

已结题课题:

主持：

1.国家863数字医疗项目“医学知识库与临床决策支持系统”课题，课题编号：2012AA02A602；课题年度：2012-2015；总经费：1295万；承担任务：课题负责人

2.上海市“浦江人才计划”项目，“针对个性化医疗的信息整合数据库”,2008～2010,项目主持人，20万元，（项目编号：08PJ14084）

3.国家人保部留学回国人员科技活动择优资助重点项目，2008～2009“microRNA调控网络研究”,项目主持人，10万元

4.国家高技术研究发展计划（863计划）目标导向类项目，“建立基于临床医疗信息共享平台的医疗决策支持系统”，2006～2008,项目主持人，480万元，(项目编号：2006AA02Z344）

发表论文

Journal Articles:

1. Li, F., Zhou, L., Wang, Y., Chen, C., Yang, S., Shan, F., &Liu, L. (2022). Modeling long-range dependencies for weakly supervised disease classification and localization on chest X-ray. Quant Imaging Med Surg 2022;12(6):3364-3378.

2. Sun, X., Xu, H, Liu, G.,Chen, J., Xu, J., Li, M., Liu, L. (2022). A Robust Immuno-Prognostic Model of Non-Muscle-Invasive Bladder Cancer Indicates Dynamic Interaction in Tumor Immune Microenvironment Contributes to Cancer Progression. Front Genet. 2022 Jun 3; 13:833989.

3. Zhu, C., Yang, Z., Xia, X., Li, N., Zhong, F., & Liu, L. (2022). Multimodal reasoning based on knowledge graph embedding for specific diseases. Bioinformatics, 38(8), 2235-2245.

4. Zhang R, Liu Z, Chang X, Gao Y, Han H, Liu X, Cai H, Fu Q, Liu L, Yin K. (2022). Clinical significance of chromosomal integrity in gastric cancers.. Int J Biol Markers, Jun 19.

5. Liu, Y., Fu, Q., Peng, X., Zhu, C., Liu, G., & Liu, L. (2021). Attention-Based Deep Multiple-Instance Learning for Classifying Circular RNA and Other Long Non-Coding RNA. Genes, 12(12), 2018. https://doi.org/10.3390/genes12122018

6. Liu, G., Liu, Z., Sun, X., Xia, X., Liu, Y., & Liu, L. (2021). Pan-Cancer Genome-Wide DNA Methylation Analyses Revealed That Hypermethylation Influences 3D Architecture and Gene Expression Dysregulation in HOXA Locus During Carcinogenesis of Cancers. Frontiers in cell and developmental biology, https://doi.org/10.3389/fcell.2021.649168

7. Shi, L., Shi, W., Peng, X., Zhan, Y., Zhou, L., Wang, Y., Feng, M., Zhao, J., Shan, F., & Liu, L. (2021). Development and Validation a Nomogram Incorporating CT Radiomics Signatures and Radiological Features for Differentiating Invasive Adenocarcinoma From Adenocarcinoma In Situ and Minimally Invasive Adenocarcinoma Presenting as Ground-Glass Nodules Measuring 5-10mm in Diameter. Frontiers in oncology, 11, 618677. https://doi.org/10.3389/fonc.2021.618677

8. Wang, Y. , Wang, K. , Peng, X. , Shi, L. , & Liu, L. . (2021). Deepsdm: boundary-aware pneumothorax segmentation in chest x-ray images. Neurocomputing, 454(3)

9. Liu, X., Wu, A., Wang, X., Liu, Y., Xu, Y., Liu, G., & Liu, L. (2021). Identification of metabolism-associated molecular subtype in ovarian cancer. Journal of cellular and molecular medicine, 25(20), 9617–9626. https://doi.org/10.1111/jcmm.16907

10. Peng, X., Yang, S., Zhou, L., Mei, Y., Shi, L., Zhang, R., Shan, F., & Liu, L. (2021). Repeatability and Reproducibility of Computed Tomography Radiomics for Pulmonary Nodules: A Multicenter Phantom Study. Investigative radiology, 10.1097/RLI.0000000000000834. Advance online publication. https://doi.org/10.1097/RLI.0000000000000834

11. Shi, L., Zhao, J., Peng, X., Wang, Y., Liu, L., & Sheng, M. (2021). CT-based radiomics for differentiating invasive adenocarcinomas from indolent lung adenocarcinomas appearing as ground-glass nodules: Asystematic review. European journal of radiology, 144, 109956. https://doi.org/10.1016/j.ejrad.2021.109956

12. Xu, W., Guo, W., Lu, P., Ma, D., Liu, L., & Yu, F. (2021). Identification of an autophagy-related gene signature predicting overall survival for hepatocellular carcinoma. Bioscience reports, 41(1), BSR20203231. https://doi.org/10.1042/BSR20203231

13. Xu, W., Chen, Z., Liu, G., Dai, Y., Xu, X., Ma, D., & Liu, L. (2021). Identification of a Potential PPAR-Related Multigene Signature Predicting Prognosis of Patients with Hepatocellular Carcinoma. PPAR research, 2021, 6642939. https://doi.org/10.1155/2021/6642939

14. Gang Liu#, Wenhui Xie#, Mingming Jin#, Ping Li, Liu Liu, Lei Liu$, Gang Huang$. Transcriptomic analysis reveals a WNT signaling pathway-based gene signature prognostic for non-small cell carcinoma. Aging (Albany NY). 2020 Oct 7;12(19):19159-19172. doi: 10.18632/aging.103724.

15. Liu X, Liu G, Chen L, Liu F, Zhang X, Liu D, Liu X, Cheng X, Liu L. Untargeted Metabolomic Characterization of Ovarian Tumors. Cancers (Basel). 2020 Dec 4;12(12):3642. doi: 10.3390/cancers12123642. https://pubmed.ncbi.nlm.nih.gov/33291756/

16. Cui, D., Liu, Y., Liu, G., and Liu, L. (2020). A Multiple-Instance Learning-Based Convolutional Neural Network Model to Detect the IDH1 Mutation in the Histopathology Images of Glioma Tissues. Journal of computational biology : a journal of computational molecular cell biology.

17. Liu, Y., Dou, Y., Lu, F., and Liu, L. (2020). A study of radiomics parameters from dual-energy computed tomography images for lymph node metastasis evaluation in colorectal mucinous adenocarcinoma. Medicine 99, e19251.

18. Ren, H., Zhou, L., Liu, G., Peng, X., Shi, W., Xu, H., Shan, F., and Liu, L. (2020). An unsupervised semi-automated pulmonary nodule segmentation method based on enhanced region growing. Quantitative Imaging in Medicine and Surgery 10, 233-+.

19. Xu, W.F., Liu, Z.H., Ren, H., Peng, X.Q., Wu, A.S., Ma, D., Liu, G., and Liu, L. (2020). Twenty Metabolic Genes Based Signature Predicts Survival of Glioma Patients. J Cancer 11, 441-449.

20. Chen, L., Liu, X., Li, M., Wang, S., and Cheng, X. (2020). A novel model to predict cancer﹕pecific survival in patients with early﹕tage uterine papillary serous carcinoma (UPSC).Cancer Med. 2020 Feb;9(3):988-998.Book Chapters: