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PRO3DCNN : convolutional neural network for mapping protein structure into folds
(University of Missouri--Columbia, 2019)
Motivation: SCOPe 2.07 is a dataset of 276,231 protein domains that have been partitioned into varying folds according to their shape and function. Since a protein's fold reveals valuable information about it's shape and ...
Structural modeling of the 3D genome using machine learning
(University of Missouri--Columbia, 2021)
This dissertation, submitted as a partial requirement for completion of the Doctorate of Philosophy, outlines the research performed by Max Highsmith in the BDM Lab. This work includes a functional expansion of a ...
Protein tertiary structure prediction and refinement using deep learning
(University of Missouri--Columbia, 2022)
Building the high-quality structure of a protein from its amino acid sequence has important applications in protein engineering and drug design. The problem of accurate protein three-dimensional structure prediction from ...
Protein contact distance and structure prediction driven by deep learning
(University of Missouri--Columbia, 2023)
Proteins, fundamental building blocks of living organisms, play a crucial role in various biological processes. Understanding protein structure is essential for unraveling their functions and designing therapeutics. However, ...