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Prediction of Nucleoitide Binding Peptides Using Star Graph Topological Índices
dc.contributor.author | Liu, Yong | |
dc.contributor.author | Munteanu, Cristian-Robert | |
dc.contributor.author | Fernández-Blanco, Enrique | |
dc.contributor.author | Tan, Zhiliang | |
dc.contributor.author | Santos-del-Riego, Antonino | |
dc.contributor.author | Pazos, A. | |
dc.date.accessioned | 2016-10-17T12:16:54Z | |
dc.date.available | 2016-10-17T12:16:54Z | |
dc.date.issued | 2015-08-05 | |
dc.identifier.citation | Liu Y, Munteanu CR, Fernández Blanco E, Tan Z, Santos del Riego A, Pazos A. Prediction of nucleoitide binding peptides using star graph topological indices. Mol Inform. 2015;34(11-12):736-741 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/17456 | |
dc.description.abstract | [Abstract] The nucleotide binding proteins are involved in many important cellular processes, such as transmission of genetic information or energy transfer and storage. Therefore, the screening of new peptides for this biological function is an important research topic. The current study proposes a mixed methodology to obtain the first classification model that is able to predict new nucleotide binding peptides, using only the amino acid sequence. Thus, the methodology uses a Star graph molecular descriptor of the peptide sequences and the Machine Learning technique for the best classifier. The best model represents a Random Forest classifier based on two features of the embedded and non-embedded graphs. The performance of the model is excellent, considering similar models in the field, with an Area Under the Receiver Operating Characteristic Curve (AUROC) value of 0.938 and true positive rate (TPR) of 0.886 (test subset). The prediction of new nucleotide binding peptides with this model could be useful for drug target studies in drug development. | es_ES |
dc.description.sponsorship | Red Gallega de Investigación sobre Cáncer Colorrectal; Ref. R2014/039 | es_ES |
dc.description.sponsorship | Instituto de Salud Carlos III; PI13/00280 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.uri | http://dx.doi.org/10.1002/minf.201500064 | es_ES |
dc.rights | This is the peer reviewed version of the article which has been published in final form at Wiley Online Library. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for self-archiving. | es_ES |
dc.subject | QSAR | es_ES |
dc.subject | Nucleotide binding proteins | es_ES |
dc.subject | Star Graph | es_ES |
dc.subject | Topological indice | es_ES |
dc.title | Prediction of Nucleoitide Binding Peptides Using Star Graph Topological Índices | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Molecular Informatics | es_ES |
UDC.volume | 34 | es_ES |
UDC.issue | 11-12 | es_ES |
UDC.startPage | 736 | es_ES |
UDC.endPage | 741 | es_ES |
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INIBIC-RNASA-IMEDIR - Artigos [46]
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GI-RNASA - Artigos [176]