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dc.contributor.authorKrallinger, Martin
dc.contributor.authorRabal, Obdulia
dc.contributor.authorLourenço, Anália
dc.contributor.authorOyarzabal, Julen
dc.contributor.authorValencia, Alfonso
dc.contributor.otherBarcelona Supercomputing Center
dc.identifier.citationKrallinger, M. [et al.]. Information Retrieval and Text Mining Technologies for Chemistry. "Chemical Reviews", 5 Maig 2017, vol. 117, núm. 12, p. 7673-7761.
dc.description.abstractEfficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.
dc.description.sponsorshipA.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Conselleria de Cultura, Educacion e Ordenacion Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Inigo Garcia-Yoldi for useful feedback and discussions during the preparation of the manuscript.
dc.format.extent89 p.
dc.publisherAmerican Chemical Society
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.subjectÀrees temàtiques de la UPC::Enginyeria biomèdica
dc.subject.lcshText Mining
dc.subject.lcshBiochemistry--Data processing
dc.subject.otherText mining
dc.subject.otherData repositories
dc.subject.otherChemical information
dc.titleInformation Retrieval and Text Mining Technologies for Chemistry
dc.subject.lemacTractament de textos
dc.subject.lemacBioquímica analítica
dc.description.peerreviewedPeer Reviewed
dc.rights.accessRestricted access - publisher's policy
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/654021/EU/Open Mining INfrastructure for TExt and Data/OpenMinTeD
upcommons.citation.publicationNameChemical Reviews
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Salvo que se indique lo contrario, los contenidos de esta obra estan sujetos a la licencia de Creative Commons: Reconocimiento-NoComercial-SinObraDerivada 3.0 España