Li Shen, PhD
- PROFESSOR | Neuroscience
Research Topics:Bioinformatics, Biomedical Informatics, Computational Biology, Epigenomics, Image Analysis, Mathematical Modeling of Biomedical Systems, Mathematical and Computational Biology
Dr. Shen joined Mount Sinai in 2009 with a Ph.D. in computer science. He is currently a professor of bioinformatics with dual appointments at the department of Neuroscience and the department of Artificial Intelligence and Human Health. He has made contributions to large-scale sequencing analysis, bioinformatic tools development and machine learning applications to medical imaging and epigenomics. His current research interests include the applications of machine learning in healthcare and genomics. He has more than 100 publications with >12,000 total citations. See his Google scholar page for a full list of his publications. Visit Li Shen's Laboratory of Bioinformatics for more information on his research activities.
Multi-Disciplinary Training AreasArtificial Intelligence and Emerging Technologies in Medicine [AIET], Genetics and Genomic Sciences [GGS], Neuroscience [NEU]
BS, Fudan University
PhD, Nanyang Technological University
Postdoc, University of California San Diego
Google Cloud Research Innovator
4D Technology Development
Interfaces in Science Award
ViaVoice National Campus Application Contest Excellence Prize
Dr. Shen has made many contributions to machine learning applications in medical imaging and epigenomics, including breast cancer detection and risk prediction using mammograms and automated genome annotation using ChIP-seq.
Large-scale next-generation sequencing data analysis
From 2009-present, the Shen lab has analyzed 10,000s of NGS samples with a total storage of more than 100 TB. The results have generated numerous publications in top-tier journals, such as Nature, Science, Neuron, Nature Neuroscience and Genome Biology. The Shen lab has also produced some of the most popular bioinformatic tools, such as ngs.plot (data mining and visualization of NGS data), diffReps (ChIP-seq differential analysis) and GeneOverlap (gene lists overlapping analysis).