Predicting protein structural class by incorporating patterns of over-represented k-mers into the general form of Chou's PseAAC.
Identifieur interne : 000928 ( Ncbi/Checkpoint ); précédent : 000927; suivant : 000929Predicting protein structural class by incorporating patterns of over-represented k-mers into the general form of Chou's PseAAC.
Auteurs : Yu-Fang Qin [République populaire de Chine] ; Chun-Hua Wang ; Xiao-Qing Yu ; Jie Zhu ; Tai-Gang Liu ; Xiao-Qi ZhengSource :
- Protein and peptide letters [ 1875-5305 ] ; 2012.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , chemistry : Amino Acids, Proteins.
- chemical , classification : Proteins.
- Algorithms, Computational Biology, Databases, Protein, Neural Networks, Computer, Protein Folding, Protein Structure, Tertiary, Sequence Analysis, Protein, Support Vector Machine.
Abstract
Computational prediction of protein structural class based on sequence data remains a challenging problem in current protein science. In this paper, a new feature extraction approach based on relative polypeptide composition is introduced. This approach could take into account the background distribution of a given k-mer under a Markov model of order k-2, and avoid the curse of dimensionality with the increase of k by using a T-statistic feature selection strategy. The selected features are then fed to a support vector machine to perform the prediction. To verify the performance of our method, jackknife cross-validation tests are performed on four widely used benchmark datasets. Comparison of our results with existing methods shows that our method provides satisfactory performance for structural class prediction.
DOI: 10.2174/092986612799789350
PubMed: 22316305
Affiliations:
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Predicting protein structural class by incorporating patterns of over-represented k-mers into the general form of Chou's PseAAC.</title><author><name sortKey="Qin, Yu Fang" sort="Qin, Yu Fang" uniqKey="Qin Y" first="Yu-Fang" last="Qin">Yu-Fang Qin</name><affiliation wicri:level="1"><nlm:affiliation>Department of Mathematics, Shanghai Normal University, 200234 Shanghai, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Mathematics, Shanghai Normal University, 200234 Shanghai</wicri:regionArea><wicri:noRegion>200234 Shanghai</wicri:noRegion></affiliation></author><author><name sortKey="Wang, Chun Hua" sort="Wang, Chun Hua" uniqKey="Wang C" first="Chun-Hua" last="Wang">Chun-Hua Wang</name></author><author><name sortKey="Yu, Xiao Qing" sort="Yu, Xiao Qing" uniqKey="Yu X" first="Xiao-Qing" last="Yu">Xiao-Qing Yu</name></author><author><name sortKey="Zhu, Jie" sort="Zhu, Jie" uniqKey="Zhu J" first="Jie" last="Zhu">Jie Zhu</name></author><author><name sortKey="Liu, Tai Gang" sort="Liu, Tai Gang" uniqKey="Liu T" first="Tai-Gang" last="Liu">Tai-Gang Liu</name></author><author><name sortKey="Zheng, Xiao Qi" sort="Zheng, Xiao Qi" uniqKey="Zheng X" first="Xiao-Qi" last="Zheng">Xiao-Qi Zheng</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2012">2012</date><idno type="RBID">pubmed:22316305</idno><idno type="pmid">22316305</idno><idno type="doi">10.2174/092986612799789350</idno><idno type="wicri:Area/PubMed/Corpus">001E08</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">001E08</idno><idno type="wicri:Area/PubMed/Curation">001E08</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">001E08</idno><idno type="wicri:Area/PubMed/Checkpoint">001C48</idno><idno type="wicri:explorRef" wicri:stream="Checkpoint" 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last="Wang">Chun-Hua Wang</name></author><author><name sortKey="Yu, Xiao Qing" sort="Yu, Xiao Qing" uniqKey="Yu X" first="Xiao-Qing" last="Yu">Xiao-Qing Yu</name></author><author><name sortKey="Zhu, Jie" sort="Zhu, Jie" uniqKey="Zhu J" first="Jie" last="Zhu">Jie Zhu</name></author><author><name sortKey="Liu, Tai Gang" sort="Liu, Tai Gang" uniqKey="Liu T" first="Tai-Gang" last="Liu">Tai-Gang Liu</name></author><author><name sortKey="Zheng, Xiao Qi" sort="Zheng, Xiao Qi" uniqKey="Zheng X" first="Xiao-Qi" last="Zheng">Xiao-Qi Zheng</name></author></analytic><series><title level="j">Protein and peptide letters</title><idno type="eISSN">1875-5305</idno><imprint><date when="2012" type="published">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Amino Acids (chemistry)</term><term>Computational Biology</term><term>Databases, Protein</term><term>Neural Networks, Computer</term><term>Protein Folding</term><term>Protein Structure, Tertiary</term><term>Proteins (chemistry)</term><term>Proteins (classification)</term><term>Sequence Analysis, Protein</term><term>Support Vector Machine</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Acides aminés ()</term><term>Algorithmes</term><term>Analyse de séquence de protéine</term><term>Bases de données de protéines</term><term>Biologie informatique</term><term>Machine à vecteur de support</term><term>Pliage des protéines</term><term>Protéines ()</term><term>Structure tertiaire des protéines</term></keywords><keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en"><term>Amino Acids</term><term>Proteins</term></keywords><keywords scheme="MESH" type="chemical" qualifier="classification" xml:lang="en"><term>Proteins</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Computational Biology</term><term>Databases, Protein</term><term>Neural Networks, Computer</term><term>Protein Folding</term><term>Protein Structure, Tertiary</term><term>Sequence Analysis, Protein</term><term>Support Vector Machine</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Acides aminés</term><term>Algorithmes</term><term>Analyse de séquence de protéine</term><term>Bases de données de protéines</term><term>Biologie informatique</term><term>Machine à vecteur de support</term><term>Pliage des protéines</term><term>Protéines</term><term>Structure tertiaire des protéines</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Computational prediction of protein structural class based on sequence data remains a challenging problem in current protein science. In this paper, a new feature extraction approach based on relative polypeptide composition is introduced. This approach could take into account the background distribution of a given k-mer under a Markov model of order k-2, and avoid the curse of dimensionality with the increase of k by using a T-statistic feature selection strategy. The selected features are then fed to a support vector machine to perform the prediction. To verify the performance of our method, jackknife cross-validation tests are performed on four widely used benchmark datasets. Comparison of our results with existing methods shows that our method provides satisfactory performance for structural class prediction.</div></front></TEI><affiliations><list><country><li>République populaire de Chine</li></country></list><tree><noCountry><name sortKey="Liu, Tai Gang" sort="Liu, Tai Gang" uniqKey="Liu T" first="Tai-Gang" last="Liu">Tai-Gang Liu</name><name sortKey="Wang, Chun Hua" sort="Wang, Chun Hua" uniqKey="Wang C" first="Chun-Hua" last="Wang">Chun-Hua Wang</name><name sortKey="Yu, Xiao Qing" sort="Yu, Xiao Qing" uniqKey="Yu X" first="Xiao-Qing" last="Yu">Xiao-Qing Yu</name><name sortKey="Zheng, Xiao Qi" sort="Zheng, Xiao Qi" uniqKey="Zheng X" first="Xiao-Qi" last="Zheng">Xiao-Qi Zheng</name><name sortKey="Zhu, Jie" sort="Zhu, Jie" uniqKey="Zhu J" first="Jie" last="Zhu">Jie Zhu</name></noCountry><country name="République populaire de Chine"><noRegion><name sortKey="Qin, Yu Fang" sort="Qin, Yu Fang" uniqKey="Qin Y" first="Yu-Fang" last="Qin">Yu-Fang Qin</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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