The Resource Machine learning methods for evaluating the quality of a single protein model using energy and structural properties, by Junlin Wang

Machine learning methods for evaluating the quality of a single protein model using energy and structural properties, by Junlin Wang

Label
Machine learning methods for evaluating the quality of a single protein model using energy and structural properties
Title
Machine learning methods for evaluating the quality of a single protein model using energy and structural properties
Statement of responsibility
by Junlin Wang
Creator
Contributor
Author
Thesis advisor
Subject
Genre
Language
eng
Summary
Computational protein structure prediction is one of the most important problems in bioinformatics. In the process of protein three-dimensional structure prediction, assessing the quality of generated models accurately is crucial. Although many model quality assessment (QA) methods have been developed in the past years, the accuracy of the state-of-the-art single-model QA methods is still not high enough for practical applications. Although consensus QA methods performed significantly better than single-model QA methods in the CASP (Critical Assessment of protein Structure Prediction) competitions, they require a pool of models with diverse quality to perform well. In this thesis, new machine learning based methods are developed for single-model QA and top-model selection from a pool of candidates. These methods are based on a comprehensive set of model structure features, such as matching of secondary structure and solvent accessibility, as well as existing potential or energy function scores. For each model, using these features as inputs, machine learning methods are able to predict a quality score in the range of. Five state-of-the-art machine learning algorithms are implemented, trained, and tested using CASP datasets on various QA and selection tasks. Among the five algorithms, boosting and random forest achieved the best results overall. They outperform existing single-model QA methods, including DFIRE, RW and Proq2, significantly, by up to 10% in QA scores
Cataloging source
MUU
http://library.link/vocab/creatorName
Wang, Junlin
Degree
M.S.
Dissertation note
Thesis
Dissertation year
2015.
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
1967-
http://library.link/vocab/relatedWorkOrContributorName
Shang, Yi
http://library.link/vocab/subjectName
  • Proteins
  • Structural bioinformatics
Label
Machine learning methods for evaluating the quality of a single protein model using energy and structural properties, by Junlin Wang
Instantiates
Publication
Note
Dr. Yi Shang, Thesis Supervisor
Bibliography note
Includes bibliographical references (pages 61-66)
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
997608570
Extent
1 online resource (x, 66 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)997608570
Label
Machine learning methods for evaluating the quality of a single protein model using energy and structural properties, by Junlin Wang
Publication
Note
Dr. Yi Shang, Thesis Supervisor
Bibliography note
Includes bibliographical references (pages 61-66)
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
997608570
Extent
1 online resource (x, 66 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)997608570

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