Prediction of protein-protein interaction sites by random forest algorithm with mRMR and IFS.

Prediction of protein-protein interaction (PPI) sites is one of the most challenging problems in computational biology. Although great progress has been made by employing various machine learning approaches with numerous characteristic features, the problem is still far from being solved. In this st...

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Autores principales:	Bi-Qing Li, Kai-Yan Feng, Lei Chen, Tao Huang, Yu-Dong Cai
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e8eab8813c9f453a958baa213f02f01d
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spelling	oai:doaj.org-article:e8eab8813c9f453a958baa213f02f01d2021-11-18T07:07:27ZPrediction of protein-protein interaction sites by random forest algorithm with mRMR and IFS.1932-620310.1371/journal.pone.0043927https://doaj.org/article/e8eab8813c9f453a958baa213f02f01d2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22937126/?tool=EBIhttps://doaj.org/toc/1932-6203Prediction of protein-protein interaction (PPI) sites is one of the most challenging problems in computational biology. Although great progress has been made by employing various machine learning approaches with numerous characteristic features, the problem is still far from being solved. In this study, we developed a novel predictor based on Random Forest (RF) algorithm with the Minimum Redundancy Maximal Relevance (mRMR) method followed by incremental feature selection (IFS). We incorporated features of physicochemical/biochemical properties, sequence conservation, residual disorder, secondary structure and solvent accessibility. We also included five 3D structural features to predict protein-protein interaction sites and achieved an overall accuracy of 0.672997 and MCC of 0.347977. Feature analysis showed that 3D structural features such as Depth Index (DPX) and surface curvature (SC) contributed most to the prediction of protein-protein interaction sites. It was also shown via site-specific feature analysis that the features of individual residues from PPI sites contribute most to the determination of protein-protein interaction sites. It is anticipated that our prediction method will become a useful tool for identifying PPI sites, and that the feature analysis described in this paper will provide useful insights into the mechanisms of interaction.Bi-Qing LiKai-Yan FengLei ChenTao HuangYu-Dong CaiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 8, p e43927 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Bi-Qing Li Kai-Yan Feng Lei Chen Tao Huang Yu-Dong Cai Prediction of protein-protein interaction sites by random forest algorithm with mRMR and IFS.
description	Prediction of protein-protein interaction (PPI) sites is one of the most challenging problems in computational biology. Although great progress has been made by employing various machine learning approaches with numerous characteristic features, the problem is still far from being solved. In this study, we developed a novel predictor based on Random Forest (RF) algorithm with the Minimum Redundancy Maximal Relevance (mRMR) method followed by incremental feature selection (IFS). We incorporated features of physicochemical/biochemical properties, sequence conservation, residual disorder, secondary structure and solvent accessibility. We also included five 3D structural features to predict protein-protein interaction sites and achieved an overall accuracy of 0.672997 and MCC of 0.347977. Feature analysis showed that 3D structural features such as Depth Index (DPX) and surface curvature (SC) contributed most to the prediction of protein-protein interaction sites. It was also shown via site-specific feature analysis that the features of individual residues from PPI sites contribute most to the determination of protein-protein interaction sites. It is anticipated that our prediction method will become a useful tool for identifying PPI sites, and that the feature analysis described in this paper will provide useful insights into the mechanisms of interaction.
format	article
author	Bi-Qing Li Kai-Yan Feng Lei Chen Tao Huang Yu-Dong Cai
author_facet	Bi-Qing Li Kai-Yan Feng Lei Chen Tao Huang Yu-Dong Cai
author_sort	Bi-Qing Li
title	Prediction of protein-protein interaction sites by random forest algorithm with mRMR and IFS.
title_short	Prediction of protein-protein interaction sites by random forest algorithm with mRMR and IFS.
title_full	Prediction of protein-protein interaction sites by random forest algorithm with mRMR and IFS.
title_fullStr	Prediction of protein-protein interaction sites by random forest algorithm with mRMR and IFS.
title_full_unstemmed	Prediction of protein-protein interaction sites by random forest algorithm with mRMR and IFS.
title_sort	prediction of protein-protein interaction sites by random forest algorithm with mrmr and ifs.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/e8eab8813c9f453a958baa213f02f01d
work_keys_str_mv	AT biqingli predictionofproteinproteininteractionsitesbyrandomforestalgorithmwithmrmrandifs AT kaiyanfeng predictionofproteinproteininteractionsitesbyrandomforestalgorithmwithmrmrandifs AT leichen predictionofproteinproteininteractionsitesbyrandomforestalgorithmwithmrmrandifs AT taohuang predictionofproteinproteininteractionsitesbyrandomforestalgorithmwithmrmrandifs AT yudongcai predictionofproteinproteininteractionsitesbyrandomforestalgorithmwithmrmrandifs
_version_	1718423950279573504

Prediction of protein-protein interaction sites by random forest algorithm with mRMR and IFS.

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