- -

Automatic Drug-Drug Interaction Detection: A Machine Learning Approach With Maximal Frequent Sequence Extraction

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Automatic Drug-Drug Interaction Detection: A Machine Learning Approach With Maximal Frequent Sequence Extraction

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: GARCÍA;MOLA;Danger ...
Tamaño: 109.1Kb
Formato: PDF
Abrir
dc.contributor.author García Blasco, Sandra es_ES
dc.contributor.author Mola Velasco, Santiago Moisés es_ES
dc.contributor.author Danger Mercaderes, Roxana María es_ES
dc.contributor.author Rosso, Paolo es_ES
dc.date.accessioned 2013-11-12T13:29:41Z
dc.date.issued 2011
dc.identifier.issn 1613-0073
dc.identifier.uri http://hdl.handle.net/10251/33478
dc.description.abstract [EN] A Drug-Drug Interaction (DDI) occurs when the effects of a drug are modified by the presence of other drugs. DDIExtraction2011 proposes a first challenge task, Drug-Drug Interaction Extraction, to compare different techniques for DDI extraction and to set a benchmark that will enable future systems to be tested. The goal of the competition is for every pair of drugs in a sentence, decide whether an interaction is being described or not. We built a system based on machine learning based on bag of words and pattern extraction. Bag of words and other drug-level and character-level have been proven to have a high discriminative power for detecting DDI, while pattern extraction provided a moderated improvement indicating a good line for further research. en_EN
dc.description.sponsorship This work has been done in the framework of the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems. Contributions of first and second authors have been supported and partially funded by bitsnbrains S.L. Contribution of fourth author has been partially funded by the European Commission as part of the WIQEI IRSES project (grant no. 269180) within the FP 7 Marie Curie People Framework, by MICINN as part of the Text-Enterprise 2.0 project (TIN2009-13391-C04-03) within the Plan I+D+i. Computational resources for this research have been kindly provided by Daniel Kuehn from Data@UrService.
dc.language Inglés es_ES
dc.publisher CEUR Workshop Proceedings es_ES
dc.relation.ispartof CEUR Workshop Proceedings es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Automatic Drug-Drug Interaction Detection: A Machine Learning Approach With Maximal Frequent Sequence Extraction es_ES
dc.type Artículo es_ES
dc.embargo.lift 10000-01-01
dc.embargo.terms forever es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//TIN2009-13391-C04-03/ES/Text-Enterprise 2.0: Tecnicas De Comprension De Textos Aplicadas A Las Necesidades De La Empresa 2.0/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/269180/EU/Web Information Quality Evaluation Initiative/
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation García Blasco, S.; Mola Velasco, SM.; Danger Mercaderes, RM.; Rosso, P. (2011). Automatic Drug-Drug Interaction Detection: A Machine Learning Approach With Maximal Frequent Sequence Extraction. CEUR Workshop Proceedings. 761:51-58. http://hdl.handle.net/10251/33478 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename Workshop on Drug-Drug Interaction Extraction (DDIExtraction2011) es_ES
dc.relation.conferencedate September 09, 2011 es_ES
dc.relation.conferenceplace Huelva, España es_ES
dc.relation.publisherversion http://ceur-ws.org/Vol-761/ es_ES
dc.description.upvformatpinicio 51 es_ES
dc.description.upvformatpfin 58 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 761 es_ES
dc.relation.senia 217499
dc.contributor.funder European Commission
dc.contributor.funder Ministerio de Ciencia e Innovación


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem