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dc.contributor.authorVeredas-Navarro, Francisco Javier 
dc.contributor.authorCantón, Francisco R.
dc.contributor.authorAledo-Ramos, Juan Carlos 
dc.date.accessioned2017-06-19T09:25:41Z
dc.date.available2017-06-19T09:25:41Z
dc.date.issued2017-05-18
dc.identifier.citationVeredas F.J., Cantón F.R., Aledo J.C. (2017) Prediction of Protein Oxidation Sites. In: Rojas I., Joya G., Catala A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science, vol 10306. Springer, Chames_ES
dc.identifier.urihttp://hdl.handle.net/10630/13932
dc.description.abstractAlthough reactive oxygen species are best known as damag- ing agents linked to aerobic metabolism, it is now clear that they can also function as messengers in cellular signalling processes. Methionine, one of the two sulphur containing amino acids in proteins, is liable to be oxidized by a well-known reactive oxygen species: hydrogen perox- ide. The awareness that methionine oxidation may provide a mecha- nism to the modulation of a wide range of protein functions and cellular processes has recently encouraged proteomic approaches. However, these experimental studies are considerably time-consuming, labor-intensive and expensive, thus making the development of in silico methods for predicting methionine oxidation sites highly desirable. In the field of pro- tein phosphorylation, computational prediction of phosphorylation sites has emerged as a popular alternative approach. On the other hand, very few in-silico studies for methionine oxidation prediction exist in the lit- erature. In the current study we have addressed this issue by developing predictive models based on machine learning strategies and models— random forests, support vector machines, neural networks and flexible discriminant analysis—, aimed at accurate prediction of methionine oxi- dation sites.es_ES
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.es_ES
dc.language.isoenges_ES
dc.publisherSpringer International Publishing AGes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectAdenosilmetioninaes_ES
dc.subjectSimulación por ordenadores_ES
dc.subjectOxidaciónes_ES
dc.subject.otherProteomicses_ES
dc.subject.otherPost-translational Modificationes_ES
dc.subject.otherMethionine Oxidationes_ES
dc.subject.otherComputational Predictive Modeles_ES
dc.subject.otherMachine Learninges_ES
dc.titlePrediction of Protein Oxidation Siteses_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.centroE.T.S.I. Telecomunicaciónes_ES
dc.relation.eventtitleInternational Work Conference on Artificial Neural Networks (IWANN 2017)es_ES
dc.relation.eventplaceCádiz, Españaes_ES
dc.relation.eventdateJunio, 2017es_ES
dc.cclicenseby-nc-ndes_ES


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