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Analysis of Protein Pathway Networks Using Hybrid Properties

Given a protein-forming system, i.e., a system consisting of certain number of different proteins, can it form a biologically meaningful pathway? This is a fundamental problem in systems biology and proteomics. During the past decade, a vast amount of information on different organisms, at both the...

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Autores principales: Chen, Lei, Huang, Tao, Shi, Xiao-He, Cai, Yu-Dong, Chou, Kuo-Chen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6259184/
https://www.ncbi.nlm.nih.gov/pubmed/21076385
http://dx.doi.org/10.3390/molecules15118177
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author Chen, Lei
Huang, Tao
Shi, Xiao-He
Cai, Yu-Dong
Chou, Kuo-Chen
author_facet Chen, Lei
Huang, Tao
Shi, Xiao-He
Cai, Yu-Dong
Chou, Kuo-Chen
author_sort Chen, Lei
collection PubMed
description Given a protein-forming system, i.e., a system consisting of certain number of different proteins, can it form a biologically meaningful pathway? This is a fundamental problem in systems biology and proteomics. During the past decade, a vast amount of information on different organisms, at both the genetic and metabolic levels, has been accumulated and systematically stored in various specific databases, such as KEGG, ENZYME, BRENDA, EcoCyc and MetaCyc. These data have made it feasible to address such an essential problem. In this paper, we have analyzed known regulatory pathways in humans by extracting different (biological and graphic) features from each of the 17,069 protein-formed systems, of which 169 are positive pathways, i.e., known regulatory pathways taken from KEGG; while 16,900 were negative, i.e., not formed as a biologically meaningful pathway. Each of these protein-forming systems was represented by 352 features, of which 88 are graph features and 264 biological features. To analyze these features, the “Minimum Redundancy Maximum Relevance” and the “Incremental Feature Selection” techniques were utilized to select a set of 22 optimal features to query whether a protein-forming system is able to form a biologically meaningful pathway or not. It was found through cross-validation that the overall success rate thus obtained in identifying the positive pathways was 79.88%. It is anticipated that, this novel approach and encouraging result, although preliminary yet, may stimulate extensive investigations into this important topic.
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spelling pubmed-62591842018-12-06 Analysis of Protein Pathway Networks Using Hybrid Properties Chen, Lei Huang, Tao Shi, Xiao-He Cai, Yu-Dong Chou, Kuo-Chen Molecules Article Given a protein-forming system, i.e., a system consisting of certain number of different proteins, can it form a biologically meaningful pathway? This is a fundamental problem in systems biology and proteomics. During the past decade, a vast amount of information on different organisms, at both the genetic and metabolic levels, has been accumulated and systematically stored in various specific databases, such as KEGG, ENZYME, BRENDA, EcoCyc and MetaCyc. These data have made it feasible to address such an essential problem. In this paper, we have analyzed known regulatory pathways in humans by extracting different (biological and graphic) features from each of the 17,069 protein-formed systems, of which 169 are positive pathways, i.e., known regulatory pathways taken from KEGG; while 16,900 were negative, i.e., not formed as a biologically meaningful pathway. Each of these protein-forming systems was represented by 352 features, of which 88 are graph features and 264 biological features. To analyze these features, the “Minimum Redundancy Maximum Relevance” and the “Incremental Feature Selection” techniques were utilized to select a set of 22 optimal features to query whether a protein-forming system is able to form a biologically meaningful pathway or not. It was found through cross-validation that the overall success rate thus obtained in identifying the positive pathways was 79.88%. It is anticipated that, this novel approach and encouraging result, although preliminary yet, may stimulate extensive investigations into this important topic. MDPI 2010-11-12 /pmc/articles/PMC6259184/ /pubmed/21076385 http://dx.doi.org/10.3390/molecules15118177 Text en © 2010 by the authors; http://creativecommons.org/licenses/by/3.0/ licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Chen, Lei
Huang, Tao
Shi, Xiao-He
Cai, Yu-Dong
Chou, Kuo-Chen
Analysis of Protein Pathway Networks Using Hybrid Properties
title Analysis of Protein Pathway Networks Using Hybrid Properties
title_full Analysis of Protein Pathway Networks Using Hybrid Properties
title_fullStr Analysis of Protein Pathway Networks Using Hybrid Properties
title_full_unstemmed Analysis of Protein Pathway Networks Using Hybrid Properties
title_short Analysis of Protein Pathway Networks Using Hybrid Properties
title_sort analysis of protein pathway networks using hybrid properties
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6259184/
https://www.ncbi.nlm.nih.gov/pubmed/21076385
http://dx.doi.org/10.3390/molecules15118177
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