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Mathematical deep learning prediction of biomolecular properties

Subject:Mathematical deep learning prediction of biomolecular properties
Speaker:Prof Wei Guowei(Michigan State University)
Time:3:00pm,26th May.,2017
Place:lecture hall,4F,Multifunctional building of Institute,WIPM

 

About the speaker:
Guowei Wei, Professor of Mathematics, Professor of Electrical & Computer Engineering, Adjunct Professor of Biochemistry & Molecular Biology at the department of mathematics, Michigan State University. His research interests include: mathematical biophysics and molecular biosciences, big data, machine learning and structural bioinformatics, biomedical image and surface analysis, high order interface methods, nano modeling and simulation, quantum kinetic theory, etc. Prof. Wei published more than 200 papers and his H-index reached 54.

 

Abstract:
Biology is believed to be the last forefront of natural sciences. The exponential growth of biological data has paved the way for biological sciences to transform from qualitative, phenomenological and descriptive to quantitative, analytical and predictive. Mathematics, including machine learning, has become a driving force behind this historic transformation as it did to quantum physics a century ago. I will discuss how to combine differential geometry, algebraic topology, graph theory and partial differential equation with machine learning and deep learning to arrive at the cutting edge predictions of a vast variety of molecular and biomolecular properties, including solvation free energies, partition coefficients, protein-drug binding affinities, protein mutation impacts and protein folds.

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Wuhan Institute of Physics and Mathematics, CAS
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