Data-restrained structural modeling based on experimental information has attracted new attention. Solutions to such problems are still under development. The use of experimental restraints, such as cross-linking/mass spectrometry, small-angle x-ray scattering (SAXS) and single-particle cryo-electron microscopy (cryo-EM) have emerged and are considered a promising direction in structural modeling improvement.
My research interest is focused on developing computational methods (i.e. deep learning, machine learning, optimization techniques) to determine the best ways to fully leverage the experimental information from SAXS/Cryo-EM in protein structure modeling.
An introduction to computer programming motivated by the analysis of biological data sets and the modeling of biological systems. Computing conceptsto include data representation, control structures, text processing, input andoutput. Applications to include the representation and analysis of proteinand genetic sequences, and the use of available biological data sets.