Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/94818
DC FieldValueLanguage
dc.contributor.authorTung, Chi-Huazh_TW
dc.contributor.authorChen, Chi-Weizh_TW
dc.contributor.authorGuo, Ren-Chaozh_TW
dc.contributor.authorNg, Hui-Fuangzh_TW
dc.contributor.authorChu, Yen-Weizh_TW
dc.date2016-
dc.date.accessioned2018-04-30T07:29:05Z-
dc.date.available2018-04-30T07:29:05Z-
dc.identifier.urihttp://hdl.handle.net/11455/94818-
dc.description.abstractBackground. Quaternary structures of proteins are closely relevant to gene regulation, signal transduction, and many other biological functions of proteins. In the current study, a new method based on protein-conserved motif composition in block format for feature extraction is proposed, which is termed block composition. Results. The protein quaternary assembly states prediction system which combines blocks with functional domain composition, called QuaBingo, is constructed by three layers of classifiers that can categorize quaternary structural attributes of monomer, homooligomer, and heterooligomer. The building of the first layer classifier uses support vector machines (SVM) based on blocks and functional domains of proteins, and the second layer SVM was utilized to process the outputs of the first layer. Finally, the result is determined by the Random Forest of the third layer. We compared the effectiveness of the combination of block composition, functional domain composition, and pseudoamino acid composition of the model. In the 11 kinds of functional protein families, QuaBingo is 23% of Matthews Correlation Coefficient (MCC) higher than the existing prediction system. The results also revealed the biological characterization of the top five block compositions. Conclusions. QuaBingo provides better predictive ability for predicting the quaternary structural attributes of proteins.zh_TW
dc.language.isoenzh_TW
dc.relationBioMed research international, Volume 2016, Page(s) 9480276.zh_TW
dc.subjectAmino Acid Sequencezh_TW
dc.subjectComputer Simulationzh_TW
dc.subjectModels, Chemicalzh_TW
dc.subjectMolecular Sequence Datazh_TW
dc.subjectPattern Recognition, Automatedzh_TW
dc.subjectProteinszh_TW
dc.subjectSequence Analysis, Proteinzh_TW
dc.subjectSupport Vector Machinezh_TW
dc.subjectAlgorithmszh_TW
dc.subjectModels, Molecularzh_TW
dc.subjectProtein Structure, Quaternaryzh_TW
dc.titleQuaBingo: A Prediction System for Protein Quaternary Structure Attributes Using Block Compositionzh_TW
dc.typeJournal Articlezh_TW
dc.identifier.doi10.1155/2016/9480276zh_TW
item.grantfulltextnone-
item.openairetypeJournal Article-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.fulltextno fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
Appears in Collections:基因體暨生物資訊學研究所
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