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...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad de Medellín
- Repositorio:
- Repositorio UDEM
- Idioma:
- eng
- OAI Identifier:
- oai:repository.udem.edu.co:11407/5648
- Acceso en línea:
- http://hdl.handle.net/11407/5648
- Palabra clave:
- Biological data
Clustering
Entropy
Graph
Metagenomic binning
Spike sorting
Biomedical engineering
Biophysics
Entropy
Graphic methods
Machine learning
Unsupervised learning
Biological data
Clustering
Graph
Metagenomic binning
Spike-sorting
Sorting
- Rights
- License
- http://purl.org/coar/access_right/c_16ec
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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 Biological data Clustering Entropy Graph Metagenomic binning Spike sorting 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.subject.none.fl_str_mv |
Biological data Clustering Entropy Graph Metagenomic binning Spike sorting Biomedical engineering Biophysics Entropy Graphic methods Machine learning Unsupervised learning Biological data Clustering Graph Metagenomic binning Spike-sorting Sorting |
| topic |
Biological data Clustering Entropy Graph Metagenomic binning Spike sorting 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 |
2020 |
| dc.date.accessioned.none.fl_str_mv |
2020-04-29T14:53:34Z |
| dc.date.available.none.fl_str_mv |
2020-04-29T14:53:34Z |
| dc.date.none.fl_str_mv |
2020 |
| dc.type.eng.fl_str_mv |
Conference Paper |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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info:eu-repo/semantics/article |
| dc.identifier.isbn.none.fl_str_mv |
9783030306472 |
| dc.identifier.issn.none.fl_str_mv |
16800737 |
| dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/11407/5648 |
| dc.identifier.doi.none.fl_str_mv |
10.1007/978-3-030-30648-9_41 |
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9783030306472 16800737 10.1007/978-3-030-30648-9_41 |
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http://hdl.handle.net/11407/5648 |
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eng |
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eng |
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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 |
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75 |
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315 |
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321 |
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Vogt, J.E., Unsupervised structure detection in biomedical data (2015) IEEE/ACM Trans. Comput. Biol. Bioinforma., 12 (4), pp. 753-760. , https://doi.org/10.1109/TCBB.2015.2394408 Xu, R., Wunsch, D.C., Clustering algorithms in biomedical research: A review (2010) IEEE Rev. Biomed. Eng., 3, pp. 120-154. , https://doi.org/10.1109/RBME.2010.2083647 Fortunato, S., Hric, D., Community detection in networks: A user guide (2016) Phys. Rep., 659, pp. 1-44. , https://doi.org/10.1016/j.physrep.2016.09.002 de Arruda, G.F., Costa, L.D.F., Rodrigues, F.A., A complex networks approach for data clustering (2012) Phys. a Stat. Mech. Appl., 391 (23), pp. 6174-6183. , https://doi.org/10.1016/j.physa.2012.07.007 Zhang, H., Chen, X., Network-based clustering and embedding for high-dimensional data visualization (2013) 2013 International Conference on Computer-Aided Design and Computer Graphics, pp. 290-297. , https://doi.org/10.1109/CADGraphics.2013.45 Grünwald, P.D., (2007) The Minimum Description Length Principle, , The MIT Press, Cambridge Yao, J., Dash, M., Tan, S.T., Liu, H., Entropy-based fuzzy clustering and fuzzy modeling (2000) Fuzzy Sets Syst, 113 (3), pp. 381-388. , https://doi.org/10.1016/S0165-0114(98)00038-4 Laskaris, N.A., Zafeiriou, S.P., Beyond FCM: Graph-theoretic post-processing algorithms for learning and representing the data structure (2008) Pattern Recognit, 41 (8), pp. 2630-2644. , https://doi.org/10.1016/j.patcog.2008.02.005 Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E., Fast unfolding of communities in large networks (2008) J. Stat. Mech. Theory Exp., 2008 (10). , https://doi.org/10.1088/1742-5468/2008/10/P10008 Leung, H.C., Yiu, S.M., Yang, B., Peng, Y., Wang, Y., Liu, Z., Chen, J., Chin, F.Y., A robust and accurate binning algorithm for metagenomic sequences with arbitrary species abundance ratio (2011) Bioinformatics, 27 (11), pp. 1489-1495. , https://doi.org/10.1093/bioinformatics/btr186 Ceballos, J., Ariza-Jiménez, L., Pinel, N., Standardized approaches for assessing metagenomic contig binning performance from Barnes-Hut t-Stochastic neighbor embeddings (2020) VIII Latin American Conference on Biomedical Engineering and XLII National Conference on Biomedical Engineering, pp. 761-768. , https://doi.org/10.1007/978-3-030-30648-9101, pp., Springer Nature Switzerland AG Ariza-Jiménez, L., Quintero, O., Pinel, N., Unsupervised fuzzy binning of metagenomic sequence fragments on three-dimensional Barnes-Hut t-Stochastic neighbor embeddings (2018) 40Th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1315-1318. , https://doi.org/10.1109/EMBC.2018.8512529 van der Maaten, L., Accelerating t-SNE using tree-based algorithms (2014) J. Mach. Learn. Res., 15, pp. 3221-3245. , http://jmlr.org/papers/v15/vandermaaten14a.html Chaure, F.J., Rey, H.G., Quian Quiroga, R., A novel and fully automatic spike-sorting implementation with variable number of features (2018) J. Neurophysiol., 120 (4), pp. 1859-1871. , https://doi.org/10.1152/jn.00339.2018 Pedreira, C., Martinez, J., Ison, M.J., Quian Quiroga, R., How many neurons can we see with current spike sorting algorithms? (2012) J. Neurosci. Methods, 211 (1), pp. 58-65. , https://doi.org/10.1016/j.jneumeth.2012.07.010 Newman, M.E., Clauset, A., Structure and inference in annotated networks (2016) Nat. Commun., 7 (May), pp. 1-11. , https://doi.org/10.1038/ncomms11863 Sharon, I., Morowitz, M.J., Thomas, B.C., Costello, E.K., Relman, D.A., Banfield, J.F., Time series community genomics analysis reveals rapid shifts in bacterial species, strains, and phage during infant gut colonization (2013) Genome Res, 23 (1), pp. 111-120. , https://doi.org/10.1101/gr.142315.112s |
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| dc.publisher.none.fl_str_mv |
Springer |
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Ingeniería de Sistemas |
| dc.publisher.faculty.none.fl_str_mv |
Facultad de Ingenierías |
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Springer |
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IFMBE Proceedings |
| institution |
Universidad de Medellín |
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Repositorio Institucional Universidad de Medellin |
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repositorio@udem.edu.co |
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1814159100838150144 |
| spelling |
20202020-04-29T14:53:34Z2020-04-29T14:53:34Z978303030647216800737http://hdl.handle.net/11407/564810.1007/978-3-030-30648-9_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.engSpringerIngeniería de SistemasFacultad de Ingenieríashttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85075700831&doi=10.1007%2f978-3-030-30648-9_41&partnerID=40&md5=930b9690370f241ec1412784c1f71f7075315321Vogt, J.E., Unsupervised structure detection in biomedical data (2015) IEEE/ACM Trans. Comput. Biol. Bioinforma., 12 (4), pp. 753-760. , https://doi.org/10.1109/TCBB.2015.2394408Xu, R., Wunsch, D.C., Clustering algorithms in biomedical research: A review (2010) IEEE Rev. Biomed. Eng., 3, pp. 120-154. , https://doi.org/10.1109/RBME.2010.2083647Fortunato, S., Hric, D., Community detection in networks: A user guide (2016) Phys. Rep., 659, pp. 1-44. , https://doi.org/10.1016/j.physrep.2016.09.002de Arruda, G.F., Costa, L.D.F., Rodrigues, F.A., A complex networks approach for data clustering (2012) Phys. a Stat. Mech. Appl., 391 (23), pp. 6174-6183. , https://doi.org/10.1016/j.physa.2012.07.007Zhang, H., Chen, X., Network-based clustering and embedding for high-dimensional data visualization (2013) 2013 International Conference on Computer-Aided Design and Computer Graphics, pp. 290-297. , https://doi.org/10.1109/CADGraphics.2013.45Grünwald, P.D., (2007) The Minimum Description Length Principle, , The MIT Press, CambridgeYao, J., Dash, M., Tan, S.T., Liu, H., Entropy-based fuzzy clustering and fuzzy modeling (2000) Fuzzy Sets Syst, 113 (3), pp. 381-388. , https://doi.org/10.1016/S0165-0114(98)00038-4Laskaris, N.A., Zafeiriou, S.P., Beyond FCM: Graph-theoretic post-processing algorithms for learning and representing the data structure (2008) Pattern Recognit, 41 (8), pp. 2630-2644. , https://doi.org/10.1016/j.patcog.2008.02.005Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E., Fast unfolding of communities in large networks (2008) J. Stat. Mech. Theory Exp., 2008 (10). , https://doi.org/10.1088/1742-5468/2008/10/P10008Leung, H.C., Yiu, S.M., Yang, B., Peng, Y., Wang, Y., Liu, Z., Chen, J., Chin, F.Y., A robust and accurate binning algorithm for metagenomic sequences with arbitrary species abundance ratio (2011) Bioinformatics, 27 (11), pp. 1489-1495. , https://doi.org/10.1093/bioinformatics/btr186Ceballos, J., Ariza-Jiménez, L., Pinel, N., Standardized approaches for assessing metagenomic contig binning performance from Barnes-Hut t-Stochastic neighbor embeddings (2020) VIII Latin American Conference on Biomedical Engineering and XLII National Conference on Biomedical Engineering, pp. 761-768. , https://doi.org/10.1007/978-3-030-30648-9101, pp., Springer Nature Switzerland AGAriza-Jiménez, L., Quintero, O., Pinel, N., Unsupervised fuzzy binning of metagenomic sequence fragments on three-dimensional Barnes-Hut t-Stochastic neighbor embeddings (2018) 40Th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1315-1318. , https://doi.org/10.1109/EMBC.2018.8512529van der Maaten, L., Accelerating t-SNE using tree-based algorithms (2014) J. Mach. Learn. Res., 15, pp. 3221-3245. , http://jmlr.org/papers/v15/vandermaaten14a.htmlChaure, F.J., Rey, H.G., Quian Quiroga, R., A novel and fully automatic spike-sorting implementation with variable number of features (2018) J. Neurophysiol., 120 (4), pp. 1859-1871. , https://doi.org/10.1152/jn.00339.2018Pedreira, C., Martinez, J., Ison, M.J., Quian Quiroga, R., How many neurons can we see with current spike sorting algorithms? (2012) J. Neurosci. Methods, 211 (1), pp. 58-65. , https://doi.org/10.1016/j.jneumeth.2012.07.010Newman, M.E., Clauset, A., Structure and inference in annotated networks (2016) Nat. Commun., 7 (May), pp. 1-11. , https://doi.org/10.1038/ncomms11863Sharon, I., Morowitz, M.J., Thomas, B.C., Costello, E.K., Relman, D.A., Banfield, J.F., Time series community genomics analysis reveals rapid shifts in bacterial species, strains, and phage during infant gut colonization (2013) Genome Res, 23 (1), pp. 111-120. , https://doi.org/10.1101/gr.142315.112sIFMBE ProceedingsBiological dataClusteringEntropyGraphMetagenomic binningSpike sortingBiomedical engineeringBiophysicsEntropyGraphic methodsMachine learningUnsupervised learningBiological dataClusteringGraphMetagenomic binningSpike-sortingSortingAn Entropy-Based Graph Construction Method for Representing and Clustering Biological DataConference Paperinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Ariza-Jiménez, L., Mathematical Modeling Research Group, Universidad EAFIT, Medellín, Colombia; Pinel, N., Biodiversity, Evolution, and Conservation Research Group, Universidad EAFIT, Medellín, Colombia; Villa, L.F., System Engineering Research Group, ARKADIUS, Universidad de Medellín, Medellín, Colombia; Quintero, O.L., Mathematical Modeling Research Group, Universidad EAFIT, Medellín, Colombiahttp://purl.org/coar/access_right/c_16ecAriza-Jiménez L.Pinel N.Villa L.F.Quintero O.L.11407/5648oai:repository.udem.edu.co:11407/56482020-05-27 15:40:34.099Repositorio Institucional Universidad de Medellinrepositorio@udem.edu.co |