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SENT: semantic features in text

dc.contributor.authorVázquez, Miguel
dc.contributor.authorCarmona Saez, Pedro
dc.contributor.authorNogales Cadenas, Ruben
dc.contributor.authorChagoyen, Mónica
dc.contributor.authorTirado Fernández, José Francisco
dc.contributor.authorCarazo, José María
dc.contributor.authorPascual Montano, Alberto
dc.date.accessioned2023-06-20T04:05:24Z
dc.date.available2023-06-20T04:05:24Z
dc.date.issued2009-07
dc.description© 2009 The Author(s). Spanish grants [BIO2007-67150-C03-02, S-Gen-0166/2006, TIN2005-5619, PS-010000-2008-1]; European Union Grant [FP7-HEALTH-F4-2008-202047]. Funding for open access charge: Spanish Grant number BIO2007-67150-C03-02.
dc.description.abstractMedical imaging has become an absolutely essential diagnostic tool for clinical practices; at present, pathologies can be detected with an earliness never before known. Its use has not only been relegated to the field of radiology but also, increasingly, to computer-based imaging processes prior to surgery. Motion analysis, in particular, plays an important role in analyzing activities or behaviors of live objects in medicine. This short paper presents several low-cost hardware implementation approaches for the new generation of tablets and/or smartphones for estimating motion compensation and segmentation in medical images. These systems have been optimized for breast cancer diagnosis using magnetic resonance imaging technology with several advantages over traditional X-ray mammography, for example, obtaining patient information during a short period. This paper also addresses the challenge of offering a medical tool that runs on widespread portable devices, both on tablets and/or smartphones to aid in patient diagnostics.
dc.description.departmentSección Deptal. de Arquitectura de Computadores y Automática (Físicas)
dc.description.facultyFac. de Ciencias Físicas
dc.description.refereedTRUE
dc.description.sponsorshipUnión Europea. FP7
dc.description.sponsorshipIntegromics
dc.description.sponsorshipSpanish Ramon y Cajal program
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/35641
dc.identifier.doi10.1093/nar/gkp392
dc.identifier.issn0305-1048
dc.identifier.officialurlhttp://dx.doi.org/10.1093/nar/gkp392
dc.identifier.relatedurlhttp://nar.oxfordjournals.org/
dc.identifier.urihttps://hdl.handle.net/20.500.14352/44899
dc.journal.titleNucleic acids research
dc.language.isoeng
dc.page.finalW159
dc.page.initialW153
dc.publisherOxford University Press
dc.relation.projectIDRESOLVE (202047)
dc.relation.projectIDBIO2007-67150-C03-02
dc.relation.projectIDS-Gen-0166/2006
dc.relation.projectIDTIN2005-5619
dc.relation.projectIDPS-010000-2008-1
dc.rightsAtribución-NoComercial 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttps://creativecommons.org/licenses/by-nc/3.0/es/
dc.subject.cdu004
dc.subject.keywordNonnegative matrix factorization
dc.subject.keywordBiomedical literature
dc.subject.keywordMicroarray data
dc.subject.keywordGene lists
dc.subject.keywordInitialization
dc.subject.keywordNetwork
dc.subject.keywordTool.
dc.subject.ucmInformática (Informática)
dc.subject.unesco1203.17 Informática
dc.titleSENT: semantic features in text
dc.typejournal article
dc.volume.number37
dcterms.references1. Ashburner,M., Ball,C., Blake,J., Botstein,D., Butler,H., Cherry,J., Davis,A., Dolinski,K., Dwight,S., Eppig,J. et al. (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet., 25, 25–29. 2. Huang da,W., Sherman,B.T. and Lempicki,R.A. (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res., 37, 1–13. 3. Shatkay,H. and Feldman,R. (2003) Mining the biomedical literature in the genomic era: an overview. J. Comput. Biol., 10, 821–855. 4. Blaschke,C., Andrade,M.A., Ouzounis,C. and Valencia,A. (1999) Automatic extraction of biological information from scientific text: protein-protein interactions. Proc. Int. Conf. Intell. Syst. Mol. Biol., 1999, 60–67. 5. Jenssen,T.K., Laegreid,A., Komorowski,J. and Hovig,E. (2001) A literature network of human genes for high-throughput analysis of gene expression. Nat. Genet., 28, 21–28. 6. Wren,J.D. and Garner,H.R. (2004) Shared relationship analysis: ranking set cohesion and commonalities within a literature-derived relationship network. Bioinformatics, 20, 191–198. 7. Hoffmann,R. and Valencia,A. (2005) Implementing the iHOP concept for navigation of biomedical literature. Bioinformatics, 21, 252–258. 8. Chaussabel,D. and Sher,A. (2002) Mining microarray expression data by literature profiling. Genome Biol., 3, 1–16. 9. Jelier,R., Jenster,G., Dorssers,L.C.J., Wouters,B.J., Hendriksen,P.J.M., Mons,B., Delwel,R. and Kors,J.A. (2007) Text-derived concept profiles support assessment of DNA microarray data for acute myeloid leukemia and for androgen receptor stimulation. BMC Bioinformatics, 8, 14. 10. Raychaudhuri,S., Schu¨ tze,H. and Altman,R.B. (2002) Using text analysis to identify functionally coherent gene groups. Genome Res., 12, 1582–1590. 11. Huang,Z.X., Tian,H.Y., Hu,Z.F., Zhou,Y.B., Zhao,J. and Yao,K.T. (2008) GenCLiP: a software program for clustering gene lists by literature profiling and constructing gene co-occurrence networks related to custom keywords. BMC Bioinformatics, 9, 308. 12. Frijters,R., Heupers,B., van Beek,P., Bouwhuis,M., van Schaik,R., de Vlieg,J., Polman,J. and Alkema,W. (2008) CoPub: a literaturebased keyword enrichment tool for microarray data analysis. Nucleic Acids Res., 36, W406. 13. Chagoyen,M., Carmona-Saez,P., Shatkay,H., Carazo,J.M. and Pascual-Montano,A. (2006) Discovering semantic features in the literature: a foundation for building functional associations. BMC Bioinformatics, 7, 41. 14. Lee,D.D. and Seung,H.S. (1999) Learning the parts of objects by non-negative matrix factorization. Nature, 401, 788–791. 15. Pehkonen,P., Wong,G. and Toronen,P. (2005) Theme discovery from gene lists for identification and viewing of multiple functional groups. BMC Bioinformatics, 6, 16. 16. Heinrich,K.E., Berry,M.W. and Homayouni,R. (2008) Gene tree labeling using nonnegative matrix factorization on biomedical literature. Comput. Intelligence and Neuroscience, 2008, 12. 17. Tjioe,E., Berry,M. and Homayouni,R. (2008) First Workshop on Data Mining in Functional Genomics, IEEE International Conference on Bioinformatics and Biomedicine, November 3–5, 2008, Philadelphia, pp. 185–192. 18. Carmona-Saez,P., Chagoyen,M., Tirado,F., Carazo,J.M. and Pascual-Montano,A. (2007) GENECODIS: a web-based tool for finding significant concurrent annotations in gene lists. Genome Biol., 8, R3. 19. Mitchell,J.A., Aronson,A.R., Mork,J.G., Folk,L.C., Humphrey,S.M. and Ward,J.M. (2003) Gene indexing: characterization and analysis of NLM’s GeneRIFs. AMIA\ldots Annu. Symp. Proc. [electronic resource], 2003, 460. 20. Yeh,A., Morgan,A., Colosimo,M. and Hirschman,L. (2005) BioCreAtIvE task 1A: gene mention finding evaluation. BMC Bioinformatics, 6, 1. 21. Wilbur,J., Smith,L. and Tanabe,L. (2007) Biocreative 2. Gene mention task. Proc. Second BioCreative Challenge Eval. Workshop, 1, 7–16. 22. Salton,G., Wong,A. and Yang,C. (1975) A vector space model for automatic indexing. Commun. ACM, 18, 613–620. 23. Porter,M. (1980) An algorithm for suffix stripping. Program, 14, 130–137. 24. Sparck,J.K. (1988). In Willett,P. (ed.) A statistical interpretation of term specificity and its application in retrieval. Document Retrieval Systems, Taylor Graham Series in Foundations of Information Science, Vol. 3, Taylor Graham Publishing, London, UK, pp. 132–142. 25. Mejia-Roa,E., Carmona-Saez,P., Nogales,R., Vicente,C., Vazquez,M., Yang,X.Y., Garcia,C., Tirado,F. and Pascual-Montano,A. (2008) bioNMF: a web-based tool for nonnegative matrix factorization in biology. Nucleic Acids Res., 36, W523–W528. 26. Boutsidis,C. and Gallopoulos,E. (2008) SVD based initialization: a head start for nonnegative matrix factorization. Pattern Recogn., 41, 1350–1362. 27. Wild,S., Curry,J. and Dougherty,A. (2004) Improving non-negative matrix factorizations through structured initialization. Pattern Recogn., 37, 2217–2232. 28. Deerwester,S., Dumais,S., Furnas,G.W., Landauer,T.K. and Harshman,R. (1990) Indexing by latent semantic analysis. J. Am. Soc. Inform. Sci., 41, 391–407. 29. Homayouni,R., Heinrich,K., Wei,L. and Berry,M.W. (2005) Gene clustering by latent semantic indexing of MEDLINE abstracts. Bioinformatics, 21, 104–115. 30. D’Arcangelo,G., Homayouni,R., Keshvara,L., Rice,D., Sheldon,M. and Curran,T. (1999) Reelin is a ligand for lipoprotein receptors. Neuron, 24, 471–479. 31. Huang,Z.X., Tian,H.Y., Hu,Z.F., Zhou,Y.B., Zhao,J. and Yao,K.T. (2008) GenCLiP: a software program for clustering gene lists by literature profiling and constructing gene co-occurrence networks related to custom keywords. BMC Bioinformatics, 9, 308.
dspace.entity.typePublication
relation.isAuthorOfPublication1356616c-9e69-4852-8415-62fd0b8e7cfc
relation.isAuthorOfPublication.latestForDiscovery1356616c-9e69-4852-8415-62fd0b8e7cfc

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