Li Shen, PhD
- ASSOCIATE PROFESSOR | Neuroscience
Research Topics:Bioinformatics, Biomedical Informatics, Computational Biology, Epigenomics, Image Analysis, Mathematical Modeling of Biomedical Systems, Mathematical and Computational Biology
Dr. Shen joined the Department of Neuroscience at the Icahn School of Medicine at Mount Sinai in 2009 with a Ph.D. in computer science. He is currently associate professor of bioinformatics. He has made contributions to epigenomics in brain diseases and bioinformatic tools development. His current research interests include the applications of machine learning in healthcare and genomics. He has more than 100 publications with >7,700 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 AreaGenetics and Data Science [GDS]
BS, Fudan University
PhD, Nanyang Technological University
Postdoc, University of California San Diego
Burroughs Wellcome Funds for Interfaces in Science
IBM ViaVoice National Campus Application Contest Excellence Prize
Software for next-generation sequencing analysis
We have developed two popular programs: diffReps (https://github.com/shenlab-sinai/diffreps) for ChIP-seq differential analysis and ngs.plot (https://github.com/shenlab-sinai/ngsplot) for data mining and visualization of NGS data. Please visit our group Github page at: https://github.com/shenlab-sinai.
Large-scale next-generation sequencing data analysis
Our research scope includes but is not limited to: ChIP-seq, RNA-seq, small RNA-seq and DNA methyl-seq. From 2009-2016, my group analyzed more than 6,000 NGS samples with a total storage of more than 50TB. The results have generated numerous publications in top-tier journals, such as Nature, Neuron, Nature Neuroscience, Genome Biology and PNAS.
Dr. Shen has made several contributions to the open-source machine learning community: https://github.com/lishen/my-contributions-to-open-ml. He has also done some research in using machine learning for automated genome segmentation: http://dx.doi.org/10.1101/034579. Machine learning has and will always be an important tool for his research and a focus of research on its own.