NeurIPS 2019 | Machine Learning Meets Single Cell Biology (Invited Talk)
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Machine Learning Meets Single Cell Biology
Dana Pe'er
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This video is reposted for education purpose.
Abstract:
Biology is becoming a data science. Recent single-cell profiling technologies are creating a data deluge, wherein thousands of variables are measured for each of hundreds of thousands to millions of cells in a single dataset. The proliferation of single-cell genomic and imaging data is creating opportunities to apply machine learning approaches in order to construct a human cell atlas with enormous potential to uncover new biology—by describing the incredible diversity of our constituent cell populations, how they function, how this diversity emerges from a single cell and how processes go awry in disease. We will present success stories and computational challenges raised by these new data modalities, in both health and disease settings. Examples will include methods from manifold learning, probabilistic graphical models and deep learning.
Dana Pe'er (Columbia University)
Chair of Computational and Systems Biology program, Sloan Kettering Institute and Director of Alan and Sandra Gerry Center for Metastasis and Tumor Ecosystems. The Pe’er lab develops machine learning approaches for the analysis and interpretation of single cell data and uses these to study Cancer, Development and Immunology. Dana is member of Human Cell Atlas Organizing Committee and co-chair of its Analysis Working Group, recipient of the Burroughs Welcome Fund Career Award, NIH Director’s New Innovator Award, NSF CAREER award, Stand Up To Cancer Innovative Research Grant, Packard Fellow in Science and Engineering, Overton award, NIH Director’s Pioneer award, Lenfest Distinguished Faculty Award and Ernst W. Bertner Memorial Award.