DBIO Short Course: Machine Learning for Biological Physics
Biological physics is at an exciting moment where the integration of machine learning (ML) and data science tools have enabled new physical insights, better understanding, and accelerated discovery that surpass or complement traditional approaches. This tutorial section provides pedagogical lectures on the tools of ML-related methods applied to several topics that closely follow this year’s DBIO scientific programs. The attendees will learn about the use of ML on several data types from biomolecular sciences, molecular modeling, high-throughput multi-omics, cell biophysics, to single-cell imaging before the March Meeting kicks off.
Who Should Attend
Graduate students, post-docs, and other scientists interested in learning about the exciting new area of Machine Learning for Biological Physics. The tutorial talks will be very pedagogical, describing the theoretical foundations and tools of the field for a wide range of topics in biological physics. Latest developments and open questions will also be prominently featured.
- Clustering and dimensionality reduction methods applied to biomolecular ensembles
- Dimensionality reduction approaches for -omics data
- Can machine learning help systems modeling in cellular biophysics?
- Quantifying single-cell imaging data with deep learning method
- Margaret S. Cheung, Vice Chair of DBIO, Pacific Northwest National Laboratory; University of Washington, Seattle; University of Houston; Center for Theoretical Biological Physics at Rice University
- Margaret Gardel, Chair-Elect of DBIO, University of Chicago
- Ruxandra Dima, University of Cincinnati
- Purushottam Dixit, University of Florida
- Padmini Rangamani, University of California, San Diego
- Jianhua Xing, University of Pittsburgh