An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data

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dc.citation.journalTitleIfmbe Proceedingseng
dc.contributor.authorAriza-Jiménez L.
dc.contributor.authorPinel N.
dc.contributor.authorVilla L.F.
dc.contributor.authorQuintero O.L.
dc.contributor.departmentUniversidad EAFIT. Escuela de Cienciasspa
dc.contributor.researchgroupModelado Matemáticospa
dc.date.accessioned2021-04-12T14:07:18Z
dc.date.available2021-04-12T14:07:18Z
dc.date.issued2019-10-01
dc.description.abstractUnsupervised learning methods are commonly used to perform the non-trivial task of uncovering structure in biological data. However, conventional approaches rely on methods that make assumptions about data distribution and reduce the dimensionality of the input data. Here we propose the incorporation of entropy related measures into the process of constructing graph-based representations for biological datasets in order to uncover their inner structure. Experimental results demonstrated the potential of the proposed entropy-based graph data representation to cope with biological applications related to unsupervised learning problems, such as metagenomic binning and neuronal spike sorting, in which it is necessary to organize data into unknown and meaningful groups. © 2020, Springer Nature Switzerland AG.eng
dc.identifierhttps://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=9841
dc.identifier.doi10.1007/978-3-030-30648-9_41
dc.identifier.issn16800737
dc.identifier.otherSCOPUS;2-s2.0-85075700831
dc.identifier.urihttp://hdl.handle.net/10784/27809
dc.language.isoengeng
dc.publisherSPRINGER
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85075700831&doi=10.1007%2f978-3-030-30648-9_41&partnerID=40&md5=930b9690370f241ec1412784c1f71f70
dc.rightshttps://v2.sherpa.ac.uk/id/publication/issn/1680-0737
dc.sourceIfmbe Proceedings
dc.subject.keywordBiomedical engineeringeng
dc.subject.keywordBiophysicseng
dc.subject.keywordEntropyeng
dc.subject.keywordGraphic methodseng
dc.subject.keywordMachine learningeng
dc.subject.keywordUnsupervised learningeng
dc.subject.keywordBiological dataeng
dc.subject.keywordClusteringeng
dc.subject.keywordGrapheng
dc.subject.keywordMetagenomic binningeng
dc.subject.keywordSpike-sortingeng
dc.subject.keywordSortingeng
dc.titleAn Entropy-Based Graph Construction Method for Representing and Clustering Biological Dataeng
dc.typearticleeng
dc.typeinfo:eu-repo/semantics/articleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
dc.type.localArtículospa

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