Machine Learning Meets Single-Cell Biology: Insights and Challenges
Dec 10, 2019
Speakers
Dana Pe'er
Speaker · 3 followers
About
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.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 po…
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Biology
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AI & Data Science
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About NIPS 2019
Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.
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