Clustering of Gene Expression Profiles Applied to Marine Research

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UDC.coleccionInvestigaciónes_ES
UDC.conferenceTitleInternational Work-Conference on Artificial Neural Networkses_ES
UDC.departamentoCiencias da Computación e Tecnoloxías da Informaciónes_ES
UDC.endPage462es_ES
UDC.grupoInvRedes de Neuronas Artificiais e Sistemas Adaptativos -Informática Médica e Diagnóstico Radiolóxico (RNASA - IMEDIR)es_ES
UDC.issue7902es_ES
UDC.startPage453es_ES
UDC.volumeLecture Notes in Computer Science, vol 7902es_ES
dc.contributor.authorAguiar-Pulido, Vanessa
dc.contributor.authorSuárez-Ulloa, Victoria
dc.contributor.authorRivero, Daniel
dc.contributor.authorEirín-López, J.M.
dc.contributor.authorDorado, Julián
dc.date.accessioned2024-10-30T17:47:16Z
dc.date.available2024-10-30T17:47:16Z
dc.date.issued2013
dc.descriptionThis version of the conference paper has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-science/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-642-38679-4_45.es_ES
dc.descriptionConference paper presented at: 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, Puerto de la Cruz, Tenerife, Spain, June 12-14, 2013.es_ES
dc.description.abstract[Abstract]: This work presents the results of applying two clustering techniques to gene expression data from the mussel Mytilus galloprovincialis. The objective of the study presented in this paper was to cluster the different genes involved in the experiment, in order to find those most closely related based on their expression patterns. A self-organising map (SOM) and the k-means algorithm were used, partitioning the input data into nine clusters. The resulting clusters were then analysed using Gene Ontology (GO) data, obtaining results that suggest that SOM clusters could be more homogeneous than those obtained by the k-means technique.es_ES
dc.description.sponsorshipVanessa Aguiar-Pulido acknowledges the funding support for a research position by the “Plan I2C” Program from Xunta de Galicia (Spain), partially funded by the European Social Fund (ESF). José M. Eirín-López was awarded with grants by the Spanish Ministry of Economy and Competitivity (CGL2011-24812 & Ramon y Cajal Subprogramme) and by the Xunta de Galicia (10-PXIB-103-077-PR). Finally, the following projects also supported the work presented: “Galician Network for Colorectal Cancer Research” (REGICC, Ref. 2009/58) from the General Directorate of Research, Development and Innovation of Xunta de Galicia, “Ibero- American Network of the Nano-Bio-Info-Cogno Convergent Technologies”, Ibero- NBIC Network (209RT-0366) funded by CYTED (Spain), grant Ref. PIO52048, RD07/0067/0005 funded by the Carlos III Health Institute.es_ES
dc.description.sponsorshipXunta de Galicia; 10-PXIB-103-077-PRes_ES
dc.description.sponsorshipXunta de Galicia; REGICC 2009/58es_ES
dc.identifier.citationAguiar-Pulido, V., Suárez-Ulloa, V., Rivero, D., Eirín-López, J.M., Dorado, J. (2013). Clustering of Gene Expression Profiles Applied to Marine Research. In: Rojas, I., Joya, G., Gabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38679-4_45es_ES
dc.identifier.doi10.1007/978-3-642-38679-4_45
dc.identifier.isbn978-3-642-38678-7
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/2183/39887
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.relation.ispartofseriesLecture Notes in Computer Science (LNCS), including its subseries Lecture Notes in Artificial Intelligence (LNAI) and Lecture Notes in Bioinformatics (LNBI)es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/MICINN/Plan Nacional de I+D+i 2008-2011/CGL2011-24812/ES/ESPECIALIZACION IMPARTIDA POR LAS HISTONAS VARIANTES H2A.X Y H2A.Z A LA CROMATINA DE MOLUSCOS BIVALVOS: EVOLUCION DE ANIMALES PROTOSOMOS Y APLICACION EN TESTS DE GENOTOXICIDADes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/MSC//RD07%2F0067%2F0005/ES/es_ES
dc.relation.urihttps://doi.org/10.1007/978-3-642-38679-4_45es_ES
dc.rights© 2013 Springer-Verlag Berlin Heidelberg.es_ES
dc.rightsSubject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-science/policies/accepted-manuscript-terms).es_ES
dc.rights.accessRightsopen accesses_ES
dc.subjectClusteringes_ES
dc.subjectMicroarrayes_ES
dc.subjectNeural networkses_ES
dc.subjectData mininges_ES
dc.subjectBioinformaticses_ES
dc.subjectGene ontologyes_ES
dc.titleClustering of Gene Expression Profiles Applied to Marine Researches_ES
dc.typeconference outputes_ES
dspace.entity.typePublication
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relation.isAuthorOfPublicationd8e10433-ea19-4a35-8cc6-0c7b9f143a6d
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relation.isAuthorOfPublication5139dea6-2326-4384-a423-317cec26ee8a
relation.isAuthorOfPublication.latestForDiscovery32e6ea1f-7cb0-4c6d-8345-cc8625f08574

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