Bachelor of Genetics from Peking University in 1988; Master of Developmental Biology from Institute of Developmental Biology, Chinese Academy of Sciences in 1991; Ph.D. post doctoral research. Former Director of Bioinformatics at the University of Illinois at Urbana-Champaign Biotechnology Center, Assistant Professor of the Department of Zoology at the University of Illinois at Urbana-Champaign, and National Center for Supercomputing at the University of Illinois at Urbana-Champaign Application, NCSA) researcher (Faculty Fellow). After returning to China, he served as a researcher of the "Hundred Talents Program" of the Shanghai Academy of Biological Sciences, the Chinese Academy of Sciences, the director of the Department of Translational Medicine of the Shanghai Public Health Clinical Center of Fudan University (affiliated), the deputy director of the Shanghai Bioinformatics Research Center, and the medical information and medicine of the Big Data Research Institute of Fudan University. Director of Imaging Intelligent Diagnosis Institute, etc.
He is currently the executive vice president of the Institute of Intelligent Medicine (preparation) of Fudan University, academician of the International Institute of Health Science Informatics (IAHSI), deputy director of the Professional Committee of Clinical Data and Sample Resource Bank of the Chinese Research Hospital Association, and a clinical researcher of the Chinese Research Hospital Association. Vice Chairman of the Data and Sample Resource Bank Professional Committee, Standing Committee Member of the Medical Information Branch of the Chinese Medical Association, etc.
Email: liulei@fudan.edu.cn
Topics currently undertaken and participated in :
host :
1. Shanghai "Land High Construction" project, Medical Research Data Center of Shanghai Medical College, Fudan University, project number: DGF501010; year: 2020-continued, project leader, funding: 28.79 million
2. National Key R&D Program "Precision Medicine Research" key special project "Construction of Precision Medicine Knowledge Base for Disease Research", project number: 2016YFC09011900; year: 2016-2020, project chief, funding: 46.32 million.
participate:
1. National Natural Science Foundation of China Major Research Program "Big Data Driven Management and Decision Research" 2018 Integrated Project "Real World Big Data Driven Panoramic Health Care Management and Service Model Research", project number 91846302; Year: 2019-2022 , participation, funding: 7 million
2. The special fund project of Shanghai Municipal Economic and Information Commission for the development of software and integrated circuit industry "Intelligent medical cloud service research and development and industrial application based on cognitive computing" project, year: 2017-2020, participation, funding: 1.5 million.
Finished topics :
host :
1. National 863 Digital Medical Project "Medical Knowledge Base and Clinical Decision Support System" subject, subject number: 2012AA02A602; subject year: 2012-2015; total funding: 12.95 million; task: subject leader
2. Shanghai "Pujiang Talent Plan" project, "Information Integration Database for Personalized Medicine", 2008-2010, project leader, 200,000 yuan, (Project No.: 08PJ14084)
3. The Ministry of Human Security of the People's Republic of China selected the key projects for scientific and technological activities of returned overseas students, 2008-2009 "Research on microRNA Regulatory Network", project leader, 100,000 yuan
4. National high-tech research and development plan (863 plan) goal-oriented project, "Establishing a medical decision-making support system based on a clinical medical information sharing platform", 2006-2008, project leader, 4.8 million yuan, (project number: 2006AA02Z344)
Published papers
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 Analyzes 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 17 in diameter. . 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, WF, Liu, ZH, Ren, H., Peng, XQ, Wu, AS, 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: specific survival in patients with early: tage uterine papillary serous carcinoma (UPSC). Cancer Med. 2020 Feb;9(3):988-998. Book Chapters:
