Li Shen, Ph.D., FAIMBE

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Professor of Informatics in Biostatistics and Epidemiology
Senior Fellow, Penn Institute for Biomedical Informatics
Faculty, Penn Mahoney Institute for Neurosciences
Senior Fellow, Penn Leonard Davis Institute of Health Economics
Associate Director for Bioinformatics, Penn Institute for Biomedical Informatics
Deputy Director, Division of Informatics, DBEI
Faculty Director, IBI Bioinformatics Core
Department: Biostatistics and Epidemiology

Contact information
Department of Biostatistics, Epidemiology and Informatics
The Perelman School of Medicine
University of Pennsylvania
B306 Richards Building, 3700 Hamilton Walk
Philadelphia, PA 19104
Office: 215-573-2956
BS (Computer Science)
Xi'an Jiao Tong University, 1993.
MS (Computer Science)
Shanghai Jiao Tong University, 1996.
PhD (Computer Science)
Dartmouth College, 2004.
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Description of Research Expertise

Dr. Li Shen is a Professor of Informatics in the Department of Biostatistics, Epidemiology and Informatics at the Perelman School of Medicine in the University of Pennsylvania. He also holds a secondary appointment in the Department of Radiology. He is a Senior Fellow at the Penn Institute for Biomedical Informatics and the Leonard Davis Institute of Health Economics. He obtained his Ph.D. degree in Computer Science from Dartmouth College.

Dr. Shen's research interests include medical image computing, biomedical informatics, machine learning, network science, imaging genomics, multi-omics and systems biology, Alzheimer’s disease, and big data science in biomedicine. He has authored over 300 peer-reviewed articles in these fields. His work has been continuously supported by the NIH and NSF. His current research program is focused on developing and applying informatics, computing and data science methods for discovering actionable knowledge from complex biomedical and health data (e.g., genetics, omics, imaging, biomarker, outcome, EHR, health care), with applications to complex disorders such as Alzheimer’s disease.

Dr. Shen has served on a variety of scientific journal editorial boards, grant review committees, and organizing committees of professional meetings in medical image computing and biomedical informatics. He served as the Executive Director of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society between 2016 and 2019. He is a fellow of the American Institute for Medical and Biological Engineering (AIMBE), a distinguished member of the Association for Computing Machinery (ACM), and a distinguished contributor of the IEEE Computer Society.

Selected Publications

Shen L, Thompson PM: Brain imaging genomics: integrated analysis and machine learning. Proceedings of the IEEE 108(1): 125-162, 2020 Notes:

Yao Xiaohui, Risacher Shannon L, Nho Kwangsik, Saykin Andrew J, Wang Ze, Shen Li: Targeted genetic analysis of cerebral blood flow imaging phenotypes implicates the INPP5D gene. Neurobiology of aging 81: 213-221, Sep 2019 Notes:

Du L, Liu K, Zhu L, Yao X, Risacher SL, Guo L, Saykin AJ, Shen L: Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the ADNI cohort. Bioinformatics [ISMB/ECCB 2019 Issue, acceptance rate 18.9%] 35(14): i474-i483, July 2019 Notes:

Chasioti Danai, Yao Xiaohui, Zhang Pengyue, Lerner Samuel, Quinney Sara K, Ning Xia, Li Lang, Shen Li: Mining directional drug interaction effects on myopathy using the FAERS database. IEEE journal of biomedical and health informatics 23(5): 2156-2163, September 2019 Notes:

Zigon Bob, Li Huang, Yao Xiaohui, Fang Shiaofen, Hasan Mohammad Al, Yan Jingwen, Moore Jason H, Saykin Andrew J, Shen Li: GPU accelerated browser for neuroimaging genomics. Neuroinformatics Apr 2018.

Cong Shan, Risacher Shannon L, West John D, Wu Yu-Chien, Apostolova Liana G, Tallman Eileen, Rizkalla Maher, Salama Paul, Saykin Andrew J, Shen Li: Volumetric comparison of hippocampal subfields extracted from 4-minute accelerated vs. 8-minute high-resolution T2-weighted 3T MRI scans. Brain imaging and behavior January 2018.

Yao Xiaohui, Yan Jingwen, Liu Kefei, Kim Sungeun, Nho Kwangsik, Risacher Shannon L, Greene Casey S, Moore Jason H, Saykin Andrew J, Shen Li: Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules. Bioinformatics (Oxford, England) 33(20): 3250-3257, Oct 2017.

Yan Jingwen, Li Taiyong, Wang Hua, Huang Heng, Wan Jing, Nho Kwangsik, Kim Sungeun, Risacher Shannon L, Saykin Andrew J, Shen Li: Cortical surface biomarkers for predicting cognitive outcomes using group l2,1 norm. Neurobiology of aging 36 Suppl 1: S185-93, Jan 2015.

Yan Jingwen, Du Lei, Kim Sungeun, Risacher Shannon L, Huang Heng, Moore Jason H, Saykin Andrew J, Shen Li: Transcriptome-guided amyloid imaging genetic analysis via a novel structured sparse learning algorithm. Bioinformatics (Oxford, England) 30(17): i564-71, Sep 2014.

Shen Li, Thompson Paul M, Potkin Steven G, Bertram Lars, Farrer Lindsay A, Foroud Tatiana M, Green Robert C, Hu Xiaolan, Huentelman Matthew J, Kim Sungeun, Kauwe John S K, Li Qingqin, Liu Enchi, Macciardi Fabio, Moore Jason H, Munsie Leanne, Nho Kwangsik, Ramanan Vijay K, Risacher Shannon L, Stone David J, Swaminathan Shanker, Toga Arthur W, Weiner Michael W, Saykin Andrew J: Genetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkers. Brain imaging and behavior 8(2): 183-207, Jun 2014.

Shen Li, Kim Sungeun, Risacher Shannon L, Nho Kwangsik, Swaminathan Shanker, West John D, Foroud Tatiana, Pankratz Nathan, Moore Jason H, Sloan Chantel D, Huentelman Matthew J, Craig David W, Dechairo Bryan M, Potkin Steven G, Jack Clifford R, Weiner Michael W, Saykin Andrew J: Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort. NeuroImage 53(3): 1051-63, Nov 2010.

Wan Jing, Zhang Zhilin, Rao Bhaskar D, Fang Shiaofen, Yan Jingwen, Saykin Andrew J, Shen Li: Identifying the neuroanatomical basis of cognitive impairment in Alzheimer's disease by correlation- and nonlinearity-aware sparse Bayesian learning. IEEE transactions on medical imaging 33(7): 1475-87, Jul 2014.

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Last updated: 10/03/2023
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