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close this section of the library Protein folding. Proteins | Conformation. Proteins | Structure. Proteins | Analysis. Protein-protein interactions. Computational intelligence


View the PDF document Protein fold recognition, structure class prediction and more detection using computational intelligent methodologies
Author: Sharma, Ronesh Asnil.
Institution: The University of the South Pacific
Award: Ph.D.
Subject: Protein folding. Proteins | Conformation. Proteins | Structure. Proteins | Analysis. Protein-protein interactions. Computational intelligence
Date: 2018
Call No.: Pac QP 551 .S53 2019
BRN: 470354
Copyright:10-20% of this thesis may be copied without the authors written permission

Abstract: Proteins are considered as an important biological macromolecule, which play a major role in the biological processes and drug design. It is crucial to understand how these proteins function. Thus, protein classification, protein fold recognition, protein structure prediction and protein-protein interaction is an important step towards protein function prediction. In terms of machine learning, protein function prediction is characterized by solving a classification problem which heavily depends on the feature extraction and classification techniques. In this thesis, we have presented a number of novel algorithms for identifying the molecular recognition features (MoRFs) in intrinsically disordered proteins (IDPs) and we have outlined the methodology of predicting the multi-label subcellular proteins.
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