An ensemble classifier for eukaryotic protein subcellular location prediction using gene ontology categories and amino acid hydrophobicity
With the rapid increase of protein sequences in the post-genomic age, it is challenging to develop accurate and automated methods for reliably and quickly predicting their subcellular localizations. Till now, many efforts have been tried, but most of which used only a single algorithm. In this paper...
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| Veröffentlicht in: | PloS one 2012-01, Vol.7 (1), p.e31057-e31057 |
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| creator | Li, Liqi Zhang, Yuan Zou, Lingyun Li, Changqing Yu, Bo Zheng, Xiaoqi Zhou, Yue |
| description | With the rapid increase of protein sequences in the post-genomic age, it is challenging to develop accurate and automated methods for reliably and quickly predicting their subcellular localizations. Till now, many efforts have been tried, but most of which used only a single algorithm. In this paper, we proposed an ensemble classifier of KNN (k-nearest neighbor) and SVM (support vector machine) algorithms to predict the subcellular localization of eukaryotic proteins based on a voting system. The overall prediction accuracies by the one-versus-one strategy are 78.17%, 89.94% and 75.55% for three benchmark datasets of eukaryotic proteins. The improved prediction accuracies reveal that GO annotations and hydrophobicity of amino acids help to predict subcellular locations of eukaryotic proteins. |
| doi_str_mv | 10.1371/journal.pone.0031057 |
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Till now, many efforts have been tried, but most of which used only a single algorithm. In this paper, we proposed an ensemble classifier of KNN (k-nearest neighbor) and SVM (support vector machine) algorithms to predict the subcellular localization of eukaryotic proteins based on a voting system. The overall prediction accuracies by the one-versus-one strategy are 78.17%, 89.94% and 75.55% for three benchmark datasets of eukaryotic proteins. The improved prediction accuracies reveal that GO annotations and hydrophobicity of amino acids help to predict subcellular locations of eukaryotic proteins.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0031057</identifier><identifier>PMID: 22303481</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Amino acids ; Amino Acids - metabolism ; Annotations ; Automation ; Bioinformatics ; Biology ; Classifiers ; Computer Science ; Databases, Protein ; Datasets ; Eukaryotic Cells - metabolism ; Genes - genetics ; Humans ; Hydrophobic and Hydrophilic Interactions ; Hydrophobicity ; Localization ; Mathematics ; Methods ; Molecular Sequence Annotation - methods ; Ontology ; Orthopedics ; Pattern recognition ; Peptides ; Predictions ; Proteins ; Proteins - metabolism ; Subcellular Fractions - metabolism ; Support Vector Machine ; Support vector machines ; Voting</subject><ispartof>PloS one, 2012-01, Vol.7 (1), p.e31057-e31057</ispartof><rights>COPYRIGHT 2012 Public Library of Science</rights><rights>2012 Li et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Li et al. 2012</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c6067-93af22ec83893e8d1fa4280b265c867fd0c9ae886e2cdf76933dbbfa81153e3c3</citedby><cites>FETCH-LOGICAL-c6067-93af22ec83893e8d1fa4280b265c867fd0c9ae886e2cdf76933dbbfa81153e3c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3268814/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3268814/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2095,2914,23846,27903,27904,53769,53771,79346,79347</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/22303481$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Brusic, Vladimir</contributor><creatorcontrib>Li, Liqi</creatorcontrib><creatorcontrib>Zhang, Yuan</creatorcontrib><creatorcontrib>Zou, Lingyun</creatorcontrib><creatorcontrib>Li, Changqing</creatorcontrib><creatorcontrib>Yu, Bo</creatorcontrib><creatorcontrib>Zheng, Xiaoqi</creatorcontrib><creatorcontrib>Zhou, Yue</creatorcontrib><title>An ensemble classifier for eukaryotic protein subcellular location prediction using gene ontology categories and amino acid hydrophobicity</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>With the rapid increase of protein sequences in the post-genomic age, it is challenging to develop accurate and automated methods for reliably and quickly predicting their subcellular localizations. Till now, many efforts have been tried, but most of which used only a single algorithm. In this paper, we proposed an ensemble classifier of KNN (k-nearest neighbor) and SVM (support vector machine) algorithms to predict the subcellular localization of eukaryotic proteins based on a voting system. The overall prediction accuracies by the one-versus-one strategy are 78.17%, 89.94% and 75.55% for three benchmark datasets of eukaryotic proteins. The improved prediction accuracies reveal that GO annotations and hydrophobicity of amino acids help to predict subcellular locations of eukaryotic proteins.</description><subject>Algorithms</subject><subject>Amino acids</subject><subject>Amino Acids - metabolism</subject><subject>Annotations</subject><subject>Automation</subject><subject>Bioinformatics</subject><subject>Biology</subject><subject>Classifiers</subject><subject>Computer Science</subject><subject>Databases, Protein</subject><subject>Datasets</subject><subject>Eukaryotic Cells - metabolism</subject><subject>Genes - genetics</subject><subject>Humans</subject><subject>Hydrophobic and Hydrophilic Interactions</subject><subject>Hydrophobicity</subject><subject>Localization</subject><subject>Mathematics</subject><subject>Methods</subject><subject>Molecular Sequence Annotation - methods</subject><subject>Ontology</subject><subject>Orthopedics</subject><subject>Pattern recognition</subject><subject>Peptides</subject><subject>Predictions</subject><subject>Proteins</subject><subject>Proteins - metabolism</subject><subject>Subcellular Fractions - metabolism</subject><subject>Support Vector Machine</subject><subject>Support vector machines</subject><subject>Voting</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNk1trFDEUxwdRbK1-A9GAoPiway5zybwIS_GyUCh4ew2ZzMlsaibZJjPifgU_tdnutOxIHyQPCTm_80_yPzlZ9pzgJWEVeXflx-CkXW69gyXGjOCiepCdkprRRUkxe3i0PsmexHiFccF4WT7OTihlmOWcnGZ_Vg6Bi9A3FpCyMkajDQSkfUAw_pRh5wej0Db4AYxDcWwUWDtaGZD1Sg7GuxSE1qib5RiN61AHDpB3g7e-26FEQeeDgYika5HsjfNIKtOiza4NfrvxjVFm2D3NHmlpIzyb5rPs-8cP384_Ly4uP63PVxcLVeKyWtRMakpBccZrBrwlWuaU44aWheJlpVusagmcl0BVq6uyZqxtGi05IQUDpthZ9vKgu7U-isnGKAijLMc5ISQR6wPRenkltsH0yQfhpRE3Gz50QoZkiwVBSlXTXHFOsM4LImVes0pXqsBYlwA4ab2fThubHloFbgjSzkTnEWc2ovO_BKNlUs2TwJtJIPjrEeIgehP3RZAO_BhFTeq6qKqCJfLVP-T9j5uoTqb7G6d9OlbtNcUqT3bVdcVpopb3UGm00BuVvpw2aX-W8HaWkJgBfg-dHGMU669f_p-9_DFnXx-xG5B22ERvx_13i3MwP4Aq-BgD6DuPCRb7jrl1Q-w7Rkwdk9JeHNfnLum2RdhfvZ0UAQ</recordid><startdate>20120130</startdate><enddate>20120130</enddate><creator>Li, Liqi</creator><creator>Zhang, Yuan</creator><creator>Zou, Lingyun</creator><creator>Li, Changqing</creator><creator>Yu, Bo</creator><creator>Zheng, Xiaoqi</creator><creator>Zhou, Yue</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20120130</creationdate><title>An ensemble classifier for eukaryotic protein subcellular location prediction using gene ontology categories and amino acid hydrophobicity</title><author>Li, Liqi ; Zhang, Yuan ; Zou, Lingyun ; Li, Changqing ; Yu, Bo ; Zheng, Xiaoqi ; Zhou, Yue</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c6067-93af22ec83893e8d1fa4280b265c867fd0c9ae886e2cdf76933dbbfa81153e3c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Algorithms</topic><topic>Amino acids</topic><topic>Amino Acids - 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Till now, many efforts have been tried, but most of which used only a single algorithm. In this paper, we proposed an ensemble classifier of KNN (k-nearest neighbor) and SVM (support vector machine) algorithms to predict the subcellular localization of eukaryotic proteins based on a voting system. The overall prediction accuracies by the one-versus-one strategy are 78.17%, 89.94% and 75.55% for three benchmark datasets of eukaryotic proteins. The improved prediction accuracies reveal that GO annotations and hydrophobicity of amino acids help to predict subcellular locations of eukaryotic proteins.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>22303481</pmid><doi>10.1371/journal.pone.0031057</doi><tpages>e31057</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Amino acids Amino Acids - metabolism Annotations Automation Bioinformatics Biology Classifiers Computer Science Databases, Protein Datasets Eukaryotic Cells - metabolism Genes - genetics Humans Hydrophobic and Hydrophilic Interactions Hydrophobicity Localization Mathematics Methods Molecular Sequence Annotation - methods Ontology Orthopedics Pattern recognition Peptides Predictions Proteins Proteins - metabolism Subcellular Fractions - metabolism Support Vector Machine Support vector machines Voting |
| title | An ensemble classifier for eukaryotic protein subcellular location prediction using gene ontology categories and amino acid hydrophobicity |
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