TransportTP: A two-phase classification approach for membrane transporter prediction and characterization

Background Membrane transporters play crucial roles in living cells. Experimental characterization of transporters is costly and time-consuming. Current computational methods for transporter characterization still require extensive curation efforts, especially for eukaryotic organisms. We developed...
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Autor*in:	Li, Haiquan [verfasserIn] Benedito, Vagner A Udvardi, Michael K Zhao, Patrick Xuechun

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2009

Schlagwörter:	Gene Ontology Hide Markov Model Transporter Family Pfam Domain Hide Markov Model Model

Anmerkung:	© Li et al; licensee BioMed Central Ltd. 2009. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (

Übergeordnetes Werk:	Enthalten in: BMC bioinformatics - London : BioMed Central, 2000, 10(2009), 1 vom: 14. Dez.
Übergeordnetes Werk:	volume:10 ; year:2009 ; number:1 ; day:14 ; month:12

Links:	Volltext

DOI / URN:	10.1186/1471-2105-10-418

Katalog-ID:	SPR026854511

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520			\|a Background Membrane transporters play crucial roles in living cells. Experimental characterization of transporters is costly and time-consuming. Current computational methods for transporter characterization still require extensive curation efforts, especially for eukaryotic organisms. We developed a novel genome-scale transporter prediction and characterization system called TransportTP that combined homology-based and machine learning methods in a two-phase classification approach. First, traditional homology methods were employed to predict novel transporters based on sequence similarity to known classified proteins in the Transporter Classification Database (TCDB). Second, machine learning methods were used to integrate a variety of features to refine the initial predictions. A set of rules based on transporter features was developed by machine learning using well-curated proteomes as guides. Results In a cross-validation using the yeast proteome for training and the proteomes of ten other organisms for testing, TransportTP achieved an equivalent recall and precision of 81.8%, based on TransportDB, a manually annotated transporter database. In an independent test using the Arabidopsis proteome for training and four recently sequenced plant proteomes for testing, it achieved a recall of 74.6% and a precision of 73.4%, according to our manual curation. Conclusions TransportTP is the most effective tool for eukaryotic transporter characterization up to date.
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912			\|a GBV_ILN_73
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912			\|a GBV_ILN_95
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912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
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912			\|a GBV_ILN_206
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912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
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912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2031
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2057
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2190
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author_variant	h l hl v a b va vab m k u mk mku p x z px pxz
matchkey_str	article:14712105:2009----::rnprtawpaelsiiainprahommrntasotrrd
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publishDate	2009
allfields	10.1186/1471-2105-10-418 doi (DE-627)SPR026854511 (SPR)1471-2105-10-418-e DE-627 ger DE-627 rakwb eng Li, Haiquan verfasserin aut TransportTP: A two-phase classification approach for membrane transporter prediction and characterization 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Li et al; licensee BioMed Central Ltd. 2009. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Membrane transporters play crucial roles in living cells. Experimental characterization of transporters is costly and time-consuming. Current computational methods for transporter characterization still require extensive curation efforts, especially for eukaryotic organisms. We developed a novel genome-scale transporter prediction and characterization system called TransportTP that combined homology-based and machine learning methods in a two-phase classification approach. First, traditional homology methods were employed to predict novel transporters based on sequence similarity to known classified proteins in the Transporter Classification Database (TCDB). Second, machine learning methods were used to integrate a variety of features to refine the initial predictions. A set of rules based on transporter features was developed by machine learning using well-curated proteomes as guides. Results In a cross-validation using the yeast proteome for training and the proteomes of ten other organisms for testing, TransportTP achieved an equivalent recall and precision of 81.8%, based on TransportDB, a manually annotated transporter database. In an independent test using the Arabidopsis proteome for training and four recently sequenced plant proteomes for testing, it achieved a recall of 74.6% and a precision of 73.4%, according to our manual curation. Conclusions TransportTP is the most effective tool for eukaryotic transporter characterization up to date. Gene Ontology (dpeaa)DE-He213 Hide Markov Model (dpeaa)DE-He213 Transporter Family (dpeaa)DE-He213 Pfam Domain (dpeaa)DE-He213 Hide Markov Model Model (dpeaa)DE-He213 Benedito, Vagner A aut Udvardi, Michael K aut Zhao, Patrick Xuechun aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 10(2009), 1 vom: 14. Dez. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:10 year:2009 number:1 day:14 month:12 https://dx.doi.org/10.1186/1471-2105-10-418 lizenzpflichtig 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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2009 1 14 12
spelling	10.1186/1471-2105-10-418 doi (DE-627)SPR026854511 (SPR)1471-2105-10-418-e DE-627 ger DE-627 rakwb eng Li, Haiquan verfasserin aut TransportTP: A two-phase classification approach for membrane transporter prediction and characterization 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Li et al; licensee BioMed Central Ltd. 2009. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Membrane transporters play crucial roles in living cells. Experimental characterization of transporters is costly and time-consuming. Current computational methods for transporter characterization still require extensive curation efforts, especially for eukaryotic organisms. We developed a novel genome-scale transporter prediction and characterization system called TransportTP that combined homology-based and machine learning methods in a two-phase classification approach. First, traditional homology methods were employed to predict novel transporters based on sequence similarity to known classified proteins in the Transporter Classification Database (TCDB). Second, machine learning methods were used to integrate a variety of features to refine the initial predictions. A set of rules based on transporter features was developed by machine learning using well-curated proteomes as guides. Results In a cross-validation using the yeast proteome for training and the proteomes of ten other organisms for testing, TransportTP achieved an equivalent recall and precision of 81.8%, based on TransportDB, a manually annotated transporter database. In an independent test using the Arabidopsis proteome for training and four recently sequenced plant proteomes for testing, it achieved a recall of 74.6% and a precision of 73.4%, according to our manual curation. Conclusions TransportTP is the most effective tool for eukaryotic transporter characterization up to date. Gene Ontology (dpeaa)DE-He213 Hide Markov Model (dpeaa)DE-He213 Transporter Family (dpeaa)DE-He213 Pfam Domain (dpeaa)DE-He213 Hide Markov Model Model (dpeaa)DE-He213 Benedito, Vagner A aut Udvardi, Michael K aut Zhao, Patrick Xuechun aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 10(2009), 1 vom: 14. Dez. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:10 year:2009 number:1 day:14 month:12 https://dx.doi.org/10.1186/1471-2105-10-418 lizenzpflichtig 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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2009 1 14 12
allfields_unstemmed	10.1186/1471-2105-10-418 doi (DE-627)SPR026854511 (SPR)1471-2105-10-418-e DE-627 ger DE-627 rakwb eng Li, Haiquan verfasserin aut TransportTP: A two-phase classification approach for membrane transporter prediction and characterization 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Li et al; licensee BioMed Central Ltd. 2009. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Membrane transporters play crucial roles in living cells. Experimental characterization of transporters is costly and time-consuming. Current computational methods for transporter characterization still require extensive curation efforts, especially for eukaryotic organisms. We developed a novel genome-scale transporter prediction and characterization system called TransportTP that combined homology-based and machine learning methods in a two-phase classification approach. First, traditional homology methods were employed to predict novel transporters based on sequence similarity to known classified proteins in the Transporter Classification Database (TCDB). Second, machine learning methods were used to integrate a variety of features to refine the initial predictions. A set of rules based on transporter features was developed by machine learning using well-curated proteomes as guides. Results In a cross-validation using the yeast proteome for training and the proteomes of ten other organisms for testing, TransportTP achieved an equivalent recall and precision of 81.8%, based on TransportDB, a manually annotated transporter database. In an independent test using the Arabidopsis proteome for training and four recently sequenced plant proteomes for testing, it achieved a recall of 74.6% and a precision of 73.4%, according to our manual curation. Conclusions TransportTP is the most effective tool for eukaryotic transporter characterization up to date. Gene Ontology (dpeaa)DE-He213 Hide Markov Model (dpeaa)DE-He213 Transporter Family (dpeaa)DE-He213 Pfam Domain (dpeaa)DE-He213 Hide Markov Model Model (dpeaa)DE-He213 Benedito, Vagner A aut Udvardi, Michael K aut Zhao, Patrick Xuechun aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 10(2009), 1 vom: 14. Dez. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:10 year:2009 number:1 day:14 month:12 https://dx.doi.org/10.1186/1471-2105-10-418 lizenzpflichtig 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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2009 1 14 12
allfieldsGer	10.1186/1471-2105-10-418 doi (DE-627)SPR026854511 (SPR)1471-2105-10-418-e DE-627 ger DE-627 rakwb eng Li, Haiquan verfasserin aut TransportTP: A two-phase classification approach for membrane transporter prediction and characterization 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Li et al; licensee BioMed Central Ltd. 2009. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Membrane transporters play crucial roles in living cells. Experimental characterization of transporters is costly and time-consuming. Current computational methods for transporter characterization still require extensive curation efforts, especially for eukaryotic organisms. We developed a novel genome-scale transporter prediction and characterization system called TransportTP that combined homology-based and machine learning methods in a two-phase classification approach. First, traditional homology methods were employed to predict novel transporters based on sequence similarity to known classified proteins in the Transporter Classification Database (TCDB). Second, machine learning methods were used to integrate a variety of features to refine the initial predictions. A set of rules based on transporter features was developed by machine learning using well-curated proteomes as guides. Results In a cross-validation using the yeast proteome for training and the proteomes of ten other organisms for testing, TransportTP achieved an equivalent recall and precision of 81.8%, based on TransportDB, a manually annotated transporter database. In an independent test using the Arabidopsis proteome for training and four recently sequenced plant proteomes for testing, it achieved a recall of 74.6% and a precision of 73.4%, according to our manual curation. Conclusions TransportTP is the most effective tool for eukaryotic transporter characterization up to date. Gene Ontology (dpeaa)DE-He213 Hide Markov Model (dpeaa)DE-He213 Transporter Family (dpeaa)DE-He213 Pfam Domain (dpeaa)DE-He213 Hide Markov Model Model (dpeaa)DE-He213 Benedito, Vagner A aut Udvardi, Michael K aut Zhao, Patrick Xuechun aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 10(2009), 1 vom: 14. Dez. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:10 year:2009 number:1 day:14 month:12 https://dx.doi.org/10.1186/1471-2105-10-418 lizenzpflichtig 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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2009 1 14 12
allfieldsSound	10.1186/1471-2105-10-418 doi (DE-627)SPR026854511 (SPR)1471-2105-10-418-e DE-627 ger DE-627 rakwb eng Li, Haiquan verfasserin aut TransportTP: A two-phase classification approach for membrane transporter prediction and characterization 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Li et al; licensee BioMed Central Ltd. 2009. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Membrane transporters play crucial roles in living cells. Experimental characterization of transporters is costly and time-consuming. Current computational methods for transporter characterization still require extensive curation efforts, especially for eukaryotic organisms. We developed a novel genome-scale transporter prediction and characterization system called TransportTP that combined homology-based and machine learning methods in a two-phase classification approach. First, traditional homology methods were employed to predict novel transporters based on sequence similarity to known classified proteins in the Transporter Classification Database (TCDB). Second, machine learning methods were used to integrate a variety of features to refine the initial predictions. A set of rules based on transporter features was developed by machine learning using well-curated proteomes as guides. Results In a cross-validation using the yeast proteome for training and the proteomes of ten other organisms for testing, TransportTP achieved an equivalent recall and precision of 81.8%, based on TransportDB, a manually annotated transporter database. In an independent test using the Arabidopsis proteome for training and four recently sequenced plant proteomes for testing, it achieved a recall of 74.6% and a precision of 73.4%, according to our manual curation. Conclusions TransportTP is the most effective tool for eukaryotic transporter characterization up to date. Gene Ontology (dpeaa)DE-He213 Hide Markov Model (dpeaa)DE-He213 Transporter Family (dpeaa)DE-He213 Pfam Domain (dpeaa)DE-He213 Hide Markov Model Model (dpeaa)DE-He213 Benedito, Vagner A aut Udvardi, Michael K aut Zhao, Patrick Xuechun aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 10(2009), 1 vom: 14. Dez. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:10 year:2009 number:1 day:14 month:12 https://dx.doi.org/10.1186/1471-2105-10-418 lizenzpflichtig 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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2009 1 14 12
language	English
source	Enthalten in BMC bioinformatics 10(2009), 1 vom: 14. Dez. volume:10 year:2009 number:1 day:14 month:12
sourceStr	Enthalten in BMC bioinformatics 10(2009), 1 vom: 14. Dez. volume:10 year:2009 number:1 day:14 month:12
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Gene Ontology Hide Markov Model Transporter Family Pfam Domain Hide Markov Model Model
isfreeaccess_bool	false
container_title	BMC bioinformatics
authorswithroles_txt_mv	Li, Haiquan @@aut@@ Benedito, Vagner A @@aut@@ Udvardi, Michael K @@aut@@ Zhao, Patrick Xuechun @@aut@@
publishDateDaySort_date	2009-12-14T00:00:00Z
hierarchy_top_id	326644814
id	SPR026854511
language_de	englisch
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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Membrane transporters play crucial roles in living cells. Experimental characterization of transporters is costly and time-consuming. Current computational methods for transporter characterization still require extensive curation efforts, especially for eukaryotic organisms. We developed a novel genome-scale transporter prediction and characterization system called TransportTP that combined homology-based and machine learning methods in a two-phase classification approach. First, traditional homology methods were employed to predict novel transporters based on sequence similarity to known classified proteins in the Transporter Classification Database (TCDB). 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author	Li, Haiquan
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topic_title	TransportTP: A two-phase classification approach for membrane transporter prediction and characterization Gene Ontology (dpeaa)DE-He213 Hide Markov Model (dpeaa)DE-He213 Transporter Family (dpeaa)DE-He213 Pfam Domain (dpeaa)DE-He213 Hide Markov Model Model (dpeaa)DE-He213
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title	TransportTP: A two-phase classification approach for membrane transporter prediction and characterization
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title_full	TransportTP: A two-phase classification approach for membrane transporter prediction and characterization
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doi_str_mv	10.1186/1471-2105-10-418
title_sort	transporttp: a two-phase classification approach for membrane transporter prediction and characterization
title_auth	TransportTP: A two-phase classification approach for membrane transporter prediction and characterization
abstract	Background Membrane transporters play crucial roles in living cells. Experimental characterization of transporters is costly and time-consuming. Current computational methods for transporter characterization still require extensive curation efforts, especially for eukaryotic organisms. We developed a novel genome-scale transporter prediction and characterization system called TransportTP that combined homology-based and machine learning methods in a two-phase classification approach. First, traditional homology methods were employed to predict novel transporters based on sequence similarity to known classified proteins in the Transporter Classification Database (TCDB). Second, machine learning methods were used to integrate a variety of features to refine the initial predictions. A set of rules based on transporter features was developed by machine learning using well-curated proteomes as guides. Results In a cross-validation using the yeast proteome for training and the proteomes of ten other organisms for testing, TransportTP achieved an equivalent recall and precision of 81.8%, based on TransportDB, a manually annotated transporter database. In an independent test using the Arabidopsis proteome for training and four recently sequenced plant proteomes for testing, it achieved a recall of 74.6% and a precision of 73.4%, according to our manual curation. Conclusions TransportTP is the most effective tool for eukaryotic transporter characterization up to date. © Li et al; licensee BioMed Central Ltd. 2009. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
abstractGer	Background Membrane transporters play crucial roles in living cells. Experimental characterization of transporters is costly and time-consuming. Current computational methods for transporter characterization still require extensive curation efforts, especially for eukaryotic organisms. We developed a novel genome-scale transporter prediction and characterization system called TransportTP that combined homology-based and machine learning methods in a two-phase classification approach. First, traditional homology methods were employed to predict novel transporters based on sequence similarity to known classified proteins in the Transporter Classification Database (TCDB). Second, machine learning methods were used to integrate a variety of features to refine the initial predictions. A set of rules based on transporter features was developed by machine learning using well-curated proteomes as guides. Results In a cross-validation using the yeast proteome for training and the proteomes of ten other organisms for testing, TransportTP achieved an equivalent recall and precision of 81.8%, based on TransportDB, a manually annotated transporter database. In an independent test using the Arabidopsis proteome for training and four recently sequenced plant proteomes for testing, it achieved a recall of 74.6% and a precision of 73.4%, according to our manual curation. Conclusions TransportTP is the most effective tool for eukaryotic transporter characterization up to date. © Li et al; licensee BioMed Central Ltd. 2009. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
abstract_unstemmed	Background Membrane transporters play crucial roles in living cells. Experimental characterization of transporters is costly and time-consuming. Current computational methods for transporter characterization still require extensive curation efforts, especially for eukaryotic organisms. We developed a novel genome-scale transporter prediction and characterization system called TransportTP that combined homology-based and machine learning methods in a two-phase classification approach. First, traditional homology methods were employed to predict novel transporters based on sequence similarity to known classified proteins in the Transporter Classification Database (TCDB). Second, machine learning methods were used to integrate a variety of features to refine the initial predictions. A set of rules based on transporter features was developed by machine learning using well-curated proteomes as guides. Results In a cross-validation using the yeast proteome for training and the proteomes of ten other organisms for testing, TransportTP achieved an equivalent recall and precision of 81.8%, based on TransportDB, a manually annotated transporter database. In an independent test using the Arabidopsis proteome for training and four recently sequenced plant proteomes for testing, it achieved a recall of 74.6% and a precision of 73.4%, according to our manual curation. Conclusions TransportTP is the most effective tool for eukaryotic transporter characterization up to date. © Li et al; licensee BioMed Central Ltd. 2009. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
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title_short	TransportTP: A two-phase classification approach for membrane transporter prediction and characterization
url	https://dx.doi.org/10.1186/1471-2105-10-418
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author2	Benedito, Vagner A Udvardi, Michael K Zhao, Patrick Xuechun
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