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A comparison of machine learning techniques for detection of drug target articles

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Human Language and Accessibility Technologies (HULAT)es
dc.contributor.authorDanger, Roxana
dc.contributor.authorSegura-Bedmar, Isabel
dc.contributor.authorMartínez Fernández, Paloma
dc.contributor.authorRosso, Paolo
dc.date.accessioned2015-03-18T13:02:33Z
dc.date.available2015-03-18T13:02:33Z
dc.date.issued2010-12
dc.description.abstractImportant progress in treating diseases has been possible thanks to the identification of drug targets. Drug targets are the molecular structures whose abnormal activity, associated to a disease, can be modified by drugs, improving the health of patients. Pharmaceutical industry needs to give priority to their identification and validation in order to reduce the long and costly drug development times. In the last two decades, our knowledge about drugs, their mechanisms of action and drug targets has rapidly increased. Nevertheless, most of this knowledge is hidden in millions of medical articles and textbooks. Extracting knowledge from this large amount of unstructured information is a laborious job, even for human experts. Drug target articles identification, a crucial first step toward the automatic extraction of information from texts, constitutes the aim of this paper. A comparison of several machine learning techniques has been performed in order to obtain a satisfactory classifier for detecting drug target articles using semantic information from biomedical resources such as the Unified Medical Language System. The best result has been achieved by a Fuzzy Lattice Reasoning classifier, which reaches 98% of ROC area measure.en
dc.description.sponsorshipThis research paper is supported by Projects TIN2007-67407- C03-01, S-0505/TIC-0267 and MICINN project TEXT-ENTERPRISE 2.0 TIN2009-13391-C04-03 (Plan I + D + i), as well as for the Juan de la Cierva program of the MICINN of Spainen
dc.description.statusPublicado
dc.format.extent12
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationJournal of Biomedical Informatics 43 (2010) 6-December, pp. 902-913en
dc.identifier.doi10.1016/j.jbi.2010.07.010
dc.identifier.issn1532-0464
dc.identifier.publicationfirstpage902
dc.identifier.publicationissue6
dc.identifier.publicationlastpage913
dc.identifier.publicationtitleJournal of Biomedical Informaticsen
dc.identifier.publicationvolume43
dc.identifier.urihttps://hdl.handle.net/10016/20287
dc.identifier.uxxiAR/0000007456
dc.language.isoeng
dc.publisherElsevieren
dc.relation.projectIDComunidad de Madrid. S2009/TIC-1542/MA2VICMR
dc.relation.publisherversionhttp://dx.doi.org/10.1016/j.jbi.2010.07.010
dc.rights© 2010 Elsevier.en
dc.rights.accessRightsopen access
dc.subject.ecienciaInformáticaes
dc.subject.otherBiomedical text classificationen
dc.subject.otherBiomedical information retrievalen
dc.subject.otherDrug discoveryen
dc.subject.otherDrug targeten
dc.subject.otherMachine learningen
dc.subject.otherSupport Vector Machinesen
dc.subject.otherNaïve Bayesen
dc.subject.otherUnified Medical Language Systemen
dc.subject.otherMetaMapes
dc.titleA comparison of machine learning techniques for detection of drug target articlesen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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