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dc.contributor.authorLópez-Ferrando, Victor
dc.contributor.authorCruz, Xavier de la
dc.contributor.authorOrozco, Modesto
dc.contributor.authorGelpí, Josep Lluís
dc.identifier.citationLópez-Ferrando, Victor [et al.]. PMut2: a web-based tool for predicting pathological mutations on proteins. A: 3rd BSC International Doctoral Symposium. "Book of abstracts". Barcelona Supercomputing Center, 2015, p. 127-129.
dc.description.abstractAmino acid substitutions in proteins can result in an altered phenotype which might lead to a disease. PMut2 is a method that can predict whether a mutation has a pathological effect on the protein function. It uses current machine learning algorithms based on protein sequence derived information. The accuracy of PMut2 is as high as 82%, with a Matthews correlation coefficient of 0,62. PMut2 predictions can be obtained through a modern website which also allows to apply the same machine learning methodology that is used to train PMut2 to custom training sets, allowing users to build their own tailor-made predictors.
dc.format.extent3 p.
dc.publisherBarcelona Supercomputing Center
dc.relation.ispartofBSC International Doctoral Symposium (3rd: 2016: Barcelona)
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.subjectÀrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
dc.subjectÀrees temàtiques de la UPC::Ciències de la salut::Medicina
dc.subject.lcshHigh performance computing
dc.subject.lcshAmino acid sequence
dc.subject.lcshVariació (Biologia)
dc.titlePMut2: a web-based tool for predicting pathological mutations on proteins
dc.typeConference report
dc.subject.lemacCàlcul intensiu (Informàtica)
dc.subject.lemacSeqüència d'aminoàcids
dc.subject.lemacMutació (Biologia)
dc.rights.accessOpen Access
upcommons.citation.contributor3rd BSC International Doctoral Symposium
upcommons.citation.publicationNameBook of abstracts

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