Conditional random field approach to prediction of protein-protein interactions using domain information

Background For understanding cellular systems and biological networks, it is important to analyze functions and interactions of proteins and domains. Many methods for predicting protein-protein interactions have been developed. It is known that mutual information between residues at interacting site...
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Autor*in:	Hayashida, Morihiro [verfasserIn] Kamada, Mayumi Song, Jiangning Akutsu, Tatsuya

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2011

Schlagwörter:	Mutual Information Markov Random Field Protein Pair Conditional Random Field Domain Pair

Anmerkung:	© Hayashida et al; licensee BioMed Central Ltd. 2011

Übergeordnetes Werk:	Enthalten in: BMC systems biology - London : BioMed Central, 2007, 5(2011), Suppl 1 vom: 20. Juni
Übergeordnetes Werk:	volume:5 ; year:2011 ; number:Suppl 1 ; day:20 ; month:06

Links:	Volltext

DOI / URN:	10.1186/1752-0509-5-S1-S8

Katalog-ID:	SPR028411994

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520			\|a Background For understanding cellular systems and biological networks, it is important to analyze functions and interactions of proteins and domains. Many methods for predicting protein-protein interactions have been developed. It is known that mutual information between residues at interacting sites can be higher than that at non-interacting sites. It is based on the thought that amino acid residues at interacting sites have coevolved with those at the corresponding residues in the partner proteins. Several studies have shown that such mutual information is useful for identifying contact residues in interacting proteins. Results We propose novel methods using conditional random fields for predicting protein-protein interactions. We focus on the mutual information between residues, and combine it with conditional random fields. In the methods, protein-protein interactions are modeled using domain-domain interactions. We perform computational experiments using protein-protein interaction datasets for several organisms, and calculate AUC (Area Under ROC Curve) score. The results suggest that our proposed methods with and without mutual information outperform EM (Expectation Maximization) method proposed by Deng et al., which is one of the best predictors based on domain-domain interactions. Conclusions We propose novel methods using conditional random fields with and without mutual information between domains. Our methods based on domain-domain interactions are useful for predicting protein-protein interactions.
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author_variant	m h mh m k mk j s js t a ta
matchkey_str	article:17520509:2011----::odtoarnofedprahordcinfrtipoenneato
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allfields	10.1186/1752-0509-5-S1-S8 doi (DE-627)SPR028411994 (SPR)1752-0509-5-S1-S8-e DE-627 ger DE-627 rakwb eng Hayashida, Morihiro verfasserin aut Conditional random field approach to prediction of protein-protein interactions using domain information 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Hayashida et al; licensee BioMed Central Ltd. 2011 Background For understanding cellular systems and biological networks, it is important to analyze functions and interactions of proteins and domains. Many methods for predicting protein-protein interactions have been developed. It is known that mutual information between residues at interacting sites can be higher than that at non-interacting sites. It is based on the thought that amino acid residues at interacting sites have coevolved with those at the corresponding residues in the partner proteins. Several studies have shown that such mutual information is useful for identifying contact residues in interacting proteins. Results We propose novel methods using conditional random fields for predicting protein-protein interactions. We focus on the mutual information between residues, and combine it with conditional random fields. In the methods, protein-protein interactions are modeled using domain-domain interactions. We perform computational experiments using protein-protein interaction datasets for several organisms, and calculate AUC (Area Under ROC Curve) score. The results suggest that our proposed methods with and without mutual information outperform EM (Expectation Maximization) method proposed by Deng et al., which is one of the best predictors based on domain-domain interactions. Conclusions We propose novel methods using conditional random fields with and without mutual information between domains. Our methods based on domain-domain interactions are useful for predicting protein-protein interactions. Mutual Information (dpeaa)DE-He213 Markov Random Field (dpeaa)DE-He213 Protein Pair (dpeaa)DE-He213 Conditional Random Field (dpeaa)DE-He213 Domain Pair (dpeaa)DE-He213 Kamada, Mayumi aut Song, Jiangning aut Akutsu, Tatsuya aut Enthalten in BMC systems biology London : BioMed Central, 2007 5(2011), Suppl 1 vom: 20. Juni (DE-627)522897126 (DE-600)2265490-2 1752-0509 nnns volume:5 year:2011 number:Suppl 1 day:20 month:06 https://dx.doi.org/10.1186/1752-0509-5-S1-S8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2011 Suppl 1 20 06
spelling	10.1186/1752-0509-5-S1-S8 doi (DE-627)SPR028411994 (SPR)1752-0509-5-S1-S8-e DE-627 ger DE-627 rakwb eng Hayashida, Morihiro verfasserin aut Conditional random field approach to prediction of protein-protein interactions using domain information 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Hayashida et al; licensee BioMed Central Ltd. 2011 Background For understanding cellular systems and biological networks, it is important to analyze functions and interactions of proteins and domains. Many methods for predicting protein-protein interactions have been developed. It is known that mutual information between residues at interacting sites can be higher than that at non-interacting sites. It is based on the thought that amino acid residues at interacting sites have coevolved with those at the corresponding residues in the partner proteins. Several studies have shown that such mutual information is useful for identifying contact residues in interacting proteins. Results We propose novel methods using conditional random fields for predicting protein-protein interactions. We focus on the mutual information between residues, and combine it with conditional random fields. In the methods, protein-protein interactions are modeled using domain-domain interactions. We perform computational experiments using protein-protein interaction datasets for several organisms, and calculate AUC (Area Under ROC Curve) score. The results suggest that our proposed methods with and without mutual information outperform EM (Expectation Maximization) method proposed by Deng et al., which is one of the best predictors based on domain-domain interactions. Conclusions We propose novel methods using conditional random fields with and without mutual information between domains. Our methods based on domain-domain interactions are useful for predicting protein-protein interactions. Mutual Information (dpeaa)DE-He213 Markov Random Field (dpeaa)DE-He213 Protein Pair (dpeaa)DE-He213 Conditional Random Field (dpeaa)DE-He213 Domain Pair (dpeaa)DE-He213 Kamada, Mayumi aut Song, Jiangning aut Akutsu, Tatsuya aut Enthalten in BMC systems biology London : BioMed Central, 2007 5(2011), Suppl 1 vom: 20. Juni (DE-627)522897126 (DE-600)2265490-2 1752-0509 nnns volume:5 year:2011 number:Suppl 1 day:20 month:06 https://dx.doi.org/10.1186/1752-0509-5-S1-S8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2011 Suppl 1 20 06
allfields_unstemmed	10.1186/1752-0509-5-S1-S8 doi (DE-627)SPR028411994 (SPR)1752-0509-5-S1-S8-e DE-627 ger DE-627 rakwb eng Hayashida, Morihiro verfasserin aut Conditional random field approach to prediction of protein-protein interactions using domain information 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Hayashida et al; licensee BioMed Central Ltd. 2011 Background For understanding cellular systems and biological networks, it is important to analyze functions and interactions of proteins and domains. Many methods for predicting protein-protein interactions have been developed. It is known that mutual information between residues at interacting sites can be higher than that at non-interacting sites. It is based on the thought that amino acid residues at interacting sites have coevolved with those at the corresponding residues in the partner proteins. Several studies have shown that such mutual information is useful for identifying contact residues in interacting proteins. Results We propose novel methods using conditional random fields for predicting protein-protein interactions. We focus on the mutual information between residues, and combine it with conditional random fields. In the methods, protein-protein interactions are modeled using domain-domain interactions. We perform computational experiments using protein-protein interaction datasets for several organisms, and calculate AUC (Area Under ROC Curve) score. The results suggest that our proposed methods with and without mutual information outperform EM (Expectation Maximization) method proposed by Deng et al., which is one of the best predictors based on domain-domain interactions. Conclusions We propose novel methods using conditional random fields with and without mutual information between domains. Our methods based on domain-domain interactions are useful for predicting protein-protein interactions. Mutual Information (dpeaa)DE-He213 Markov Random Field (dpeaa)DE-He213 Protein Pair (dpeaa)DE-He213 Conditional Random Field (dpeaa)DE-He213 Domain Pair (dpeaa)DE-He213 Kamada, Mayumi aut Song, Jiangning aut Akutsu, Tatsuya aut Enthalten in BMC systems biology London : BioMed Central, 2007 5(2011), Suppl 1 vom: 20. Juni (DE-627)522897126 (DE-600)2265490-2 1752-0509 nnns volume:5 year:2011 number:Suppl 1 day:20 month:06 https://dx.doi.org/10.1186/1752-0509-5-S1-S8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2011 Suppl 1 20 06
allfieldsGer	10.1186/1752-0509-5-S1-S8 doi (DE-627)SPR028411994 (SPR)1752-0509-5-S1-S8-e DE-627 ger DE-627 rakwb eng Hayashida, Morihiro verfasserin aut Conditional random field approach to prediction of protein-protein interactions using domain information 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Hayashida et al; licensee BioMed Central Ltd. 2011 Background For understanding cellular systems and biological networks, it is important to analyze functions and interactions of proteins and domains. Many methods for predicting protein-protein interactions have been developed. It is known that mutual information between residues at interacting sites can be higher than that at non-interacting sites. It is based on the thought that amino acid residues at interacting sites have coevolved with those at the corresponding residues in the partner proteins. Several studies have shown that such mutual information is useful for identifying contact residues in interacting proteins. Results We propose novel methods using conditional random fields for predicting protein-protein interactions. We focus on the mutual information between residues, and combine it with conditional random fields. In the methods, protein-protein interactions are modeled using domain-domain interactions. We perform computational experiments using protein-protein interaction datasets for several organisms, and calculate AUC (Area Under ROC Curve) score. The results suggest that our proposed methods with and without mutual information outperform EM (Expectation Maximization) method proposed by Deng et al., which is one of the best predictors based on domain-domain interactions. Conclusions We propose novel methods using conditional random fields with and without mutual information between domains. Our methods based on domain-domain interactions are useful for predicting protein-protein interactions. Mutual Information (dpeaa)DE-He213 Markov Random Field (dpeaa)DE-He213 Protein Pair (dpeaa)DE-He213 Conditional Random Field (dpeaa)DE-He213 Domain Pair (dpeaa)DE-He213 Kamada, Mayumi aut Song, Jiangning aut Akutsu, Tatsuya aut Enthalten in BMC systems biology London : BioMed Central, 2007 5(2011), Suppl 1 vom: 20. Juni (DE-627)522897126 (DE-600)2265490-2 1752-0509 nnns volume:5 year:2011 number:Suppl 1 day:20 month:06 https://dx.doi.org/10.1186/1752-0509-5-S1-S8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2011 Suppl 1 20 06
allfieldsSound	10.1186/1752-0509-5-S1-S8 doi (DE-627)SPR028411994 (SPR)1752-0509-5-S1-S8-e DE-627 ger DE-627 rakwb eng Hayashida, Morihiro verfasserin aut Conditional random field approach to prediction of protein-protein interactions using domain information 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Hayashida et al; licensee BioMed Central Ltd. 2011 Background For understanding cellular systems and biological networks, it is important to analyze functions and interactions of proteins and domains. Many methods for predicting protein-protein interactions have been developed. It is known that mutual information between residues at interacting sites can be higher than that at non-interacting sites. It is based on the thought that amino acid residues at interacting sites have coevolved with those at the corresponding residues in the partner proteins. Several studies have shown that such mutual information is useful for identifying contact residues in interacting proteins. Results We propose novel methods using conditional random fields for predicting protein-protein interactions. We focus on the mutual information between residues, and combine it with conditional random fields. In the methods, protein-protein interactions are modeled using domain-domain interactions. We perform computational experiments using protein-protein interaction datasets for several organisms, and calculate AUC (Area Under ROC Curve) score. The results suggest that our proposed methods with and without mutual information outperform EM (Expectation Maximization) method proposed by Deng et al., which is one of the best predictors based on domain-domain interactions. Conclusions We propose novel methods using conditional random fields with and without mutual information between domains. Our methods based on domain-domain interactions are useful for predicting protein-protein interactions. Mutual Information (dpeaa)DE-He213 Markov Random Field (dpeaa)DE-He213 Protein Pair (dpeaa)DE-He213 Conditional Random Field (dpeaa)DE-He213 Domain Pair (dpeaa)DE-He213 Kamada, Mayumi aut Song, Jiangning aut Akutsu, Tatsuya aut Enthalten in BMC systems biology London : BioMed Central, 2007 5(2011), Suppl 1 vom: 20. Juni (DE-627)522897126 (DE-600)2265490-2 1752-0509 nnns volume:5 year:2011 number:Suppl 1 day:20 month:06 https://dx.doi.org/10.1186/1752-0509-5-S1-S8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2011 Suppl 1 20 06
language	English
source	Enthalten in BMC systems biology 5(2011), Suppl 1 vom: 20. Juni volume:5 year:2011 number:Suppl 1 day:20 month:06
sourceStr	Enthalten in BMC systems biology 5(2011), Suppl 1 vom: 20. Juni volume:5 year:2011 number:Suppl 1 day:20 month:06
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Mutual Information Markov Random Field Protein Pair Conditional Random Field Domain Pair
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container_title	BMC systems biology
authorswithroles_txt_mv	Hayashida, Morihiro @@aut@@ Kamada, Mayumi @@aut@@ Song, Jiangning @@aut@@ Akutsu, Tatsuya @@aut@@
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Many methods for predicting protein-protein interactions have been developed. It is known that mutual information between residues at interacting sites can be higher than that at non-interacting sites. It is based on the thought that amino acid residues at interacting sites have coevolved with those at the corresponding residues in the partner proteins. Several studies have shown that such mutual information is useful for identifying contact residues in interacting proteins. Results We propose novel methods using conditional random fields for predicting protein-protein interactions. We focus on the mutual information between residues, and combine it with conditional random fields. In the methods, protein-protein interactions are modeled using domain-domain interactions. We perform computational experiments using protein-protein interaction datasets for several organisms, and calculate AUC (Area Under ROC Curve) score. 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author	Hayashida, Morihiro
spellingShingle	Hayashida, Morihiro misc Mutual Information misc Markov Random Field misc Protein Pair misc Conditional Random Field misc Domain Pair Conditional random field approach to prediction of protein-protein interactions using domain information
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topic_title	Conditional random field approach to prediction of protein-protein interactions using domain information Mutual Information (dpeaa)DE-He213 Markov Random Field (dpeaa)DE-He213 Protein Pair (dpeaa)DE-He213 Conditional Random Field (dpeaa)DE-He213 Domain Pair (dpeaa)DE-He213
topic	misc Mutual Information misc Markov Random Field misc Protein Pair misc Conditional Random Field misc Domain Pair
topic_unstemmed	misc Mutual Information misc Markov Random Field misc Protein Pair misc Conditional Random Field misc Domain Pair
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title	Conditional random field approach to prediction of protein-protein interactions using domain information
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title_full	Conditional random field approach to prediction of protein-protein interactions using domain information
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author_browse	Hayashida, Morihiro Kamada, Mayumi Song, Jiangning Akutsu, Tatsuya
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author-letter	Hayashida, Morihiro
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title_sort	conditional random field approach to prediction of protein-protein interactions using domain information
title_auth	Conditional random field approach to prediction of protein-protein interactions using domain information
abstract	Background For understanding cellular systems and biological networks, it is important to analyze functions and interactions of proteins and domains. Many methods for predicting protein-protein interactions have been developed. It is known that mutual information between residues at interacting sites can be higher than that at non-interacting sites. It is based on the thought that amino acid residues at interacting sites have coevolved with those at the corresponding residues in the partner proteins. Several studies have shown that such mutual information is useful for identifying contact residues in interacting proteins. Results We propose novel methods using conditional random fields for predicting protein-protein interactions. We focus on the mutual information between residues, and combine it with conditional random fields. In the methods, protein-protein interactions are modeled using domain-domain interactions. We perform computational experiments using protein-protein interaction datasets for several organisms, and calculate AUC (Area Under ROC Curve) score. The results suggest that our proposed methods with and without mutual information outperform EM (Expectation Maximization) method proposed by Deng et al., which is one of the best predictors based on domain-domain interactions. Conclusions We propose novel methods using conditional random fields with and without mutual information between domains. Our methods based on domain-domain interactions are useful for predicting protein-protein interactions. © Hayashida et al; licensee BioMed Central Ltd. 2011
abstractGer	Background For understanding cellular systems and biological networks, it is important to analyze functions and interactions of proteins and domains. Many methods for predicting protein-protein interactions have been developed. It is known that mutual information between residues at interacting sites can be higher than that at non-interacting sites. It is based on the thought that amino acid residues at interacting sites have coevolved with those at the corresponding residues in the partner proteins. Several studies have shown that such mutual information is useful for identifying contact residues in interacting proteins. Results We propose novel methods using conditional random fields for predicting protein-protein interactions. We focus on the mutual information between residues, and combine it with conditional random fields. In the methods, protein-protein interactions are modeled using domain-domain interactions. We perform computational experiments using protein-protein interaction datasets for several organisms, and calculate AUC (Area Under ROC Curve) score. The results suggest that our proposed methods with and without mutual information outperform EM (Expectation Maximization) method proposed by Deng et al., which is one of the best predictors based on domain-domain interactions. Conclusions We propose novel methods using conditional random fields with and without mutual information between domains. Our methods based on domain-domain interactions are useful for predicting protein-protein interactions. © Hayashida et al; licensee BioMed Central Ltd. 2011
abstract_unstemmed	Background For understanding cellular systems and biological networks, it is important to analyze functions and interactions of proteins and domains. Many methods for predicting protein-protein interactions have been developed. It is known that mutual information between residues at interacting sites can be higher than that at non-interacting sites. It is based on the thought that amino acid residues at interacting sites have coevolved with those at the corresponding residues in the partner proteins. Several studies have shown that such mutual information is useful for identifying contact residues in interacting proteins. Results We propose novel methods using conditional random fields for predicting protein-protein interactions. We focus on the mutual information between residues, and combine it with conditional random fields. In the methods, protein-protein interactions are modeled using domain-domain interactions. We perform computational experiments using protein-protein interaction datasets for several organisms, and calculate AUC (Area Under ROC Curve) score. The results suggest that our proposed methods with and without mutual information outperform EM (Expectation Maximization) method proposed by Deng et al., which is one of the best predictors based on domain-domain interactions. Conclusions We propose novel methods using conditional random fields with and without mutual information between domains. Our methods based on domain-domain interactions are useful for predicting protein-protein interactions. © Hayashida et al; licensee BioMed Central Ltd. 2011
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title_short	Conditional random field approach to prediction of protein-protein interactions using domain information
url	https://dx.doi.org/10.1186/1752-0509-5-S1-S8
remote_bool	true
author2	Kamada, Mayumi Song, Jiangning Akutsu, Tatsuya
author2Str	Kamada, Mayumi Song, Jiangning Akutsu, Tatsuya
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doi_str	10.1186/1752-0509-5-S1-S8
up_date	2024-07-03T19:19:53.623Z
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Conditional random field approach to prediction of protein-protein interactions using domain information

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