Evaluation of Protein Backbone Alphabets : Using Predicted Local Structure for Fold Recognition

Evaluation of Protein Backbone Alphabets : Using Predicted Local Structure for Fold Recognition

Optimally combining available information is one of the key challenges in knowledge-driven prediction techniques. In this study, we evaluate six Phi and Psi-based backbone alphabets. We show that the addition of predicted backbone conformations to SVM classifiers can improve fold recognition. Our ex...

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Main Author: SHIM, Kyong Jin
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2010
Subjects:
backbone alphabet
fold recognition
local structure
protein backbone
Databases and Information Systems
Online Access:https://ink.library.smu.edu.sg/sis_research/1505
https://ink.library.smu.edu.sg/context/sis_research/article/2504/viewcontent/4257a755.pdf
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Institution: Singapore Management University
Language: English
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spelling sg-smu-ink.sis_research-25042018-08-16T06:59:35Z Evaluation of Protein Backbone Alphabets : Using Predicted Local Structure for Fold Recognition SHIM, Kyong Jin Optimally combining available information is one of the key challenges in knowledge-driven prediction techniques. In this study, we evaluate six Phi and Psi-based backbone alphabets. We show that the addition of predicted backbone conformations to SVM classifiers can improve fold recognition. Our experimental results show that the inclusion of predicted backbone conformations in our feature representation leads to higher overall accuracy compared to when using amino acid residues alone. 2010-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/1505 info:doi/10.1109/ICDMW.2010.168 https://ink.library.smu.edu.sg/context/sis_research/article/2504/viewcontent/4257a755.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University backbone alphabet fold recognition local structure protein backbone Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic backbone alphabet
fold recognition
local structure
protein backbone
Databases and Information Systems
spellingShingle backbone alphabet
fold recognition
local structure
protein backbone
Databases and Information Systems
SHIM, Kyong Jin
Evaluation of Protein Backbone Alphabets : Using Predicted Local Structure for Fold Recognition
description Optimally combining available information is one of the key challenges in knowledge-driven prediction techniques. In this study, we evaluate six Phi and Psi-based backbone alphabets. We show that the addition of predicted backbone conformations to SVM classifiers can improve fold recognition. Our experimental results show that the inclusion of predicted backbone conformations in our feature representation leads to higher overall accuracy compared to when using amino acid residues alone.
format text
author SHIM, Kyong Jin
author_facet SHIM, Kyong Jin
author_sort SHIM, Kyong Jin
title Evaluation of Protein Backbone Alphabets : Using Predicted Local Structure for Fold Recognition
title_short Evaluation of Protein Backbone Alphabets : Using Predicted Local Structure for Fold Recognition
title_full Evaluation of Protein Backbone Alphabets : Using Predicted Local Structure for Fold Recognition
title_fullStr Evaluation of Protein Backbone Alphabets : Using Predicted Local Structure for Fold Recognition
title_full_unstemmed Evaluation of Protein Backbone Alphabets : Using Predicted Local Structure for Fold Recognition
title_sort evaluation of protein backbone alphabets : using predicted local structure for fold recognition
publisher Institutional Knowledge at Singapore Management University
publishDate 2010
url https://ink.library.smu.edu.sg/sis_research/1505
https://ink.library.smu.edu.sg/context/sis_research/article/2504/viewcontent/4257a755.pdf
_version_ 1770571202237038592

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