Li Shen, Ph.D., FAIMBE

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Professor of Informatics in Biostatistics and Epidemiology
Senior Fellow, Penn Institute for Biomedical Informatics
Interim Director, Division of Informatics, DBEI
Faculty, Penn Mahoney Institute for Neurosciences
Senior Fellow, Penn Leonard Davis Institute of Health Economics
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. Shen is a computer scientist and bioinformatician. His research interests include medical image computing, bioinformatics, machine learning, network science, visual analytics, and big data science in biomedicine. The central theme of his lab is focused on developing computational and informatics methods for integrative analysis of multimodal imaging data, high throughput omics data, cognitive and other biomarker data, electronic health record data, and rich biological knowledge (e.g., pathways and networks), with applications to various complex disorders. The goal is twofold: (1) advance computer science and bioinformatics by producing novel algorithms for analyzing large scale heterogeneous data sets; and (2) provide important new insights into the phenotypic characteristics and genetic mechanisms of normal and/or disordered biological structures and functions to impact the development of new diagnostic, therapeutic and preventative approaches.

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: 09/18/2022
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