The Resource Protein structural models selection using 4-mer sequence and combined single and consensus scores, by Meshari Saud Alazmi

Protein structural models selection using 4-mer sequence and combined single and consensus scores, by Meshari Saud Alazmi

Label
Protein structural models selection using 4-mer sequence and combined single and consensus scores
Title
Protein structural models selection using 4-mer sequence and combined single and consensus scores
Statement of responsibility
by Meshari Saud Alazmi
Creator
Contributor
Author
Thesis advisor
Subject
Genre
Language
eng
Summary
Quality assessment for protein structure models is an important issue in protein structure prediction. Consensus methods assess each model based on its structural similarity to all the other models in a model set, while single scoring methods, such as Opus-ca and RW, evaluate each model based on its structural properties. In this work, a novel method proposed and developed to effectively combine consensus methods and single scoring methods for better quality assessment. At first, a new method called Single Position Specific Probability (SPSP) Score is proposed based on consensus method using 4-mer sequence. Specifically, every letter in the 4-mer sequence represents a state for a local region consisting of four amino acids. A machine learning method (Neural Network) helped to combine several single scoring methods, RW, DDFire, and OPusCa with consensus methods, SPSP and Consensus Global Distance Test-Total Score (CGDT-TS) to achieve a good combination of all the terms. The method was tested on two benchmark datasets and achieved improvements over the state-of-the-art methods. The first benchmark was on Yang Zhang's data containing 56 targets. The second benchmark was from Rosetta data containing 35 targets. For Zhang's data, the CGDT score is 0.6058, while combined method achieved 0.6105. For Rosetta data, the CGDT score achieved 0.4255, while combined method achieved 0.4529
Cataloging source
MUU
http://library.link/vocab/creatorName
Alazmi, Meshari Saud
Degree
M.S.
Dissertation note
Thesis
Dissertation year
2012.
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
1965-
http://library.link/vocab/relatedWorkOrContributorName
Xu, Dong
http://library.link/vocab/subjectName
  • Proteins
  • Machine learning
Label
Protein structural models selection using 4-mer sequence and combined single and consensus scores, by Meshari Saud Alazmi
Instantiates
Publication
Note
Advisor: Dong Xu
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
876615170
Extent
1 online resource (xiii, 98 pages)
Form of item
online
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations (chiefly color)
Specific material designation
remote
System control number
(OCoLC)876615170
Label
Protein structural models selection using 4-mer sequence and combined single and consensus scores, by Meshari Saud Alazmi
Publication
Note
Advisor: Dong Xu
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
876615170
Extent
1 online resource (xiii, 98 pages)
Form of item
online
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations (chiefly color)
Specific material designation
remote
System control number
(OCoLC)876615170

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