The Resource Probabilistic graphical models for protein structure prediction, by Debswapna Bhattacharya

Probabilistic graphical models for protein structure prediction, by Debswapna Bhattacharya

Label
Probabilistic graphical models for protein structure prediction
Title
Probabilistic graphical models for protein structure prediction
Statement of responsibility
by Debswapna Bhattacharya
Creator
Contributor
Author
Thesis advisor
Subject
Genre
Language
eng
Summary
Computationally predicting the folded and functional three-dimensional structure of a protein molecule from its amino acid sequence with high degree of accuracy is critically important in structural bioinformatics and has huge implications in understanding and curing numerous diseases caused by protein misfolding, including CJD and type II diabetes, as well as neurodegenerative diseases like Alzheimer's, Parkinson's, Huntington's, and amyotrophic lateral sclerosis. Existing computational approaches for protein structure prediction faces two key challenges: (1) difficulty in efficiently navigating the enormous conformational space accessible to proteins and (2) difficulty in accurately capturing energetics of the complex interactions of thousand of atoms in a protein molecule in silico. This dissertation attempts to address these challenges by (1) developing novel probabilistic graphical models and experimentally motivated probabilistic sampling techniques to fully capture and efficiently explore proteins' conformational space in various granularities and (2) integrating knowledge-based information into existing energy functions in order to improve their ability to discriminate correctly folded protein structures from decoys. We show that our methods outperform many traditional and state-of-the-art protein structure prediction approaches in terms of accuracy, speed and robustness. All of these methods are freely available to the scientific community
Cataloging source
MUU
http://library.link/vocab/creatorName
Bhattacharya, Debswapna
Degree
Ph. D.
Dissertation note
Thesis
Dissertation year
2016.
Government publication
government publication of a state province territory dependency etc
Granting institution
University of Missouri--Columbia
Illustrations
illustrations
Index
no index present
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
  • theses
http://library.link/vocab/relatedWorkOrContributorDate
1972-
http://library.link/vocab/relatedWorkOrContributorName
Cheng, Jianlin
http://library.link/vocab/subjectName
  • Proteins
  • Proteins
  • Proteins
  • Machine learning
  • Graphical modeling (Statistics)
Label
Probabilistic graphical models for protein structure prediction, by Debswapna Bhattacharya
Instantiates
Publication
Note
  • Dissertation supervisor: Dr. Jianlin Cheng
  • Includes vita
Bibliography note
Includes bibliographical references (pages 78-86)
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Control code
987383205
Extent
1 online resource (xi, 87 pages)
Form of item
online
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations
Specific material designation
remote
System control number
(OCoLC)987383205
Label
Probabilistic graphical models for protein structure prediction, by Debswapna Bhattacharya
Publication
Note
  • Dissertation supervisor: Dr. Jianlin Cheng
  • Includes vita
Bibliography note
Includes bibliographical references (pages 78-86)
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Control code
987383205
Extent
1 online resource (xi, 87 pages)
Form of item
online
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations
Specific material designation
remote
System control number
(OCoLC)987383205

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