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

Unsupervised 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 incorporatio...

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Autores:
Ariza-Jiménez L.
Pinel N.
Villa L.F.
Quintero O.L.
Tipo de recurso:
Fecha de publicación:
2019
Institución:
Universidad EAFIT
Repositorio:
Repositorio EAFIT
Idioma:
eng
OAI Identifier:
oai:repository.eafit.edu.co:10784/26750
Acceso en línea:
https://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=9841
http://hdl.handle.net/10784/26750
Palabra clave:
Biomedical
engineering
Biophysics
Entropy
Graphic
methods
Machine
learning
Unsupervised
learning
Biological
data
Clustering
Graph
Metagenomic
binning
Spike-sorting
Sorting
Rights
License
https://v2.sherpa.ac.uk/id/publication/issn/1680-0737
id REPOEAFIT2_ba95c38c1713a30766e05af55111e37b
oai_identifier_str oai:repository.eafit.edu.co:10784/26750
network_acronym_str REPOEAFIT2
network_name_str Repositorio EAFIT
repository_id_str
spelling 2021-03-23T19:52:10Z2019-10-012021-03-23T19:52:10Zhttps://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=984116800737SCOPUS;2-s2.0-85075700831http://hdl.handle.net/10784/2675010.1007/978-3-030-30648-9_41Unsupervised 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.engSPRINGERhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85075700831&doi=10.1007%2f978-3-030-30648-9_41&partnerID=40&md5=930b9690370f241ec1412784c1f71f70https://v2.sherpa.ac.uk/id/publication/issn/1680-0737Acceso restringidohttp://purl.org/coar/access_right/c_16ecIfmbe ProceedingsBiomedicalengineeringBiophysicsEntropyGraphicmethodsMachinelearningUnsupervisedlearningBiologicaldataClusteringGraphMetagenomicbinningSpike-sortingSortingAn Entropy-Based Graph Construction Method for Representing and Clustering Biological DatapublishedVersioninfo:eu-repo/semantics/publishedVersionarticleinfo:eu-repo/semantics/articleArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Universidad EAFIT. Departamento de CienciasAriza-Jiménez L.Pinel N.Villa L.F.Quintero O.L.Biodiversidad, Evolución y ConservaciónIfmbe Proceedings10784/26750oai:repository.eafit.edu.co:10784/267502022-04-27 15:49:59.569metadata.onlyhttps://repository.eafit.edu.coRepositorio Institucional Universidad EAFITrepositorio@eafit.edu.co
dc.title.eng.fl_str_mv An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data
title An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data
spellingShingle An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data
Biomedical
engineering
Biophysics
Entropy
Graphic
methods
Machine
learning
Unsupervised
learning
Biological
data
Clustering
Graph
Metagenomic
binning
Spike-sorting
Sorting
title_short An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data
title_full An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data
title_fullStr An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data
title_full_unstemmed An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data
title_sort An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data
dc.creator.fl_str_mv Ariza-Jiménez L.
Pinel N.
Villa L.F.
Quintero O.L.
dc.contributor.department.spa.fl_str_mv Universidad EAFIT. Departamento de Ciencias
dc.contributor.author.none.fl_str_mv Ariza-Jiménez L.
Pinel N.
Villa L.F.
Quintero O.L.
dc.contributor.researchgroup.spa.fl_str_mv Biodiversidad, Evolución y Conservación
dc.subject.eng.fl_str_mv Biomedical
engineering
Biophysics
Entropy
Graphic
methods
Machine
learning
Unsupervised
learning
Biological
data
Clustering
Graph
Metagenomic
binning
Spike-sorting
Sorting
topic Biomedical
engineering
Biophysics
Entropy
Graphic
methods
Machine
learning
Unsupervised
learning
Biological
data
Clustering
Graph
Metagenomic
binning
Spike-sorting
Sorting
description Unsupervised 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.
publishDate 2019
dc.date.issued.none.fl_str_mv 2019-10-01
dc.date.available.none.fl_str_mv 2021-03-23T19:52:10Z
dc.date.accessioned.none.fl_str_mv 2021-03-23T19:52:10Z
dc.type.eng.fl_str_mv publishedVersion
info:eu-repo/semantics/publishedVersion
article
info:eu-repo/semantics/article
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_6501
http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.local.spa.fl_str_mv Artículo
status_str publishedVersion
dc.identifier.none.fl_str_mv https://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=9841
dc.identifier.issn.none.fl_str_mv 16800737
dc.identifier.other.none.fl_str_mv SCOPUS;2-s2.0-85075700831
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/10784/26750
dc.identifier.doi.none.fl_str_mv 10.1007/978-3-030-30648-9_41
url https://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=9841
http://hdl.handle.net/10784/26750
identifier_str_mv 16800737
SCOPUS;2-s2.0-85075700831
10.1007/978-3-030-30648-9_41
dc.language.iso.eng.fl_str_mv eng
language eng
dc.relation.uri.none.fl_str_mv https://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.rights.none.fl_str_mv https://v2.sherpa.ac.uk/id/publication/issn/1680-0737
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_16ec
dc.rights.local.spa.fl_str_mv Acceso restringido
rights_invalid_str_mv https://v2.sherpa.ac.uk/id/publication/issn/1680-0737
Acceso restringido
http://purl.org/coar/access_right/c_16ec
dc.publisher.none.fl_str_mv SPRINGER
publisher.none.fl_str_mv SPRINGER
dc.source.none.fl_str_mv Ifmbe Proceedings
institution Universidad EAFIT
repository.name.fl_str_mv Repositorio Institucional Universidad EAFIT
repository.mail.fl_str_mv repositorio@eafit.edu.co
_version_ 1814110328836849664

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