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

Leandro Ariza-Jiménez, Nicolás Pinel, Luisa F. Villa, Olga Lucía Quintero

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferencia

Resumen

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.

Idioma originalInglés
Título de la publicación alojada8th Latin American Conference on Biomedical Engineering and 42nd National Conference on Biomedical Engineering - Proceedings of CLAIB-CNIB 2019
EditoresCésar A. González Díaz, Christian Chapa González, Eric Laciar Leber, Hugo A. Vélez, Norma P. Puente, Dora-Luz Flores, Adriano O. Andrade, Héctor A. Galván, Fabiola Martínez, Renato García, Citlalli J. Trujillo, Aldo R. Mejía
EditorialSpringer
Páginas315-321
Número de páginas7
ISBN (versión impresa)9783030306472
DOI
EstadoPublicada - 1 ene 2020
Evento8th Latin American Conference on Biomedical Engineering and the 42nd National Conference on Biomedical Engineering, CLAIB-CNIB 2019 - Cancún, México
Duración: 2 oct 20195 oct 2019

Serie de la publicación

NombreIFMBE Proceedings
Volumen75
ISSN (versión impresa)1680-0737
ISSN (versión digital)1433-9277

Conferencia

Conferencia8th Latin American Conference on Biomedical Engineering and the 42nd National Conference on Biomedical Engineering, CLAIB-CNIB 2019
PaísMéxico
CiudadCancún
Período2/10/195/10/19

Huella dactilar

Unsupervised learning
Entropy
Sorting

Citar esto

Ariza-Jiménez, L., Pinel, N., Villa, L. F., & Quintero, O. L. (2020). An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data. En C. A. González Díaz, C. Chapa González, E. Laciar Leber, H. A. Vélez, N. P. Puente, D-L. Flores, A. O. Andrade, H. A. Galván, F. Martínez, R. García, C. J. Trujillo, ... A. R. Mejía (Eds.), 8th Latin American Conference on Biomedical Engineering and 42nd National Conference on Biomedical Engineering - Proceedings of CLAIB-CNIB 2019 (pp. 315-321). (IFMBE Proceedings; Vol. 75). Springer. https://doi.org/10.1007/978-3-030-30648-9_41
Ariza-Jiménez, Leandro ; Pinel, Nicolás ; Villa, Luisa F. ; Quintero, Olga Lucía. / An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data. 8th Latin American Conference on Biomedical Engineering and 42nd National Conference on Biomedical Engineering - Proceedings of CLAIB-CNIB 2019. editor / César A. González Díaz ; Christian Chapa González ; Eric Laciar Leber ; Hugo A. Vélez ; Norma P. Puente ; Dora-Luz Flores ; Adriano O. Andrade ; Héctor A. Galván ; Fabiola Martínez ; Renato García ; Citlalli J. Trujillo ; Aldo R. Mejía. Springer, 2020. pp. 315-321 (IFMBE Proceedings).
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Ariza-Jiménez, L, Pinel, N, Villa, LF & Quintero, OL 2020, An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data. En CA González Díaz, C Chapa González, E Laciar Leber, HA Vélez, NP Puente, D-L Flores, AO Andrade, HA Galván, F Martínez, R García, CJ Trujillo & AR Mejía (eds.), 8th Latin American Conference on Biomedical Engineering and 42nd National Conference on Biomedical Engineering - Proceedings of CLAIB-CNIB 2019. IFMBE Proceedings, vol. 75, Springer, pp. 315-321, 8th Latin American Conference on Biomedical Engineering and the 42nd National Conference on Biomedical Engineering, CLAIB-CNIB 2019, Cancún, México, 2/10/19. https://doi.org/10.1007/978-3-030-30648-9_41

An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data. / Ariza-Jiménez, Leandro; Pinel, Nicolás; Villa, Luisa F.; Quintero, Olga Lucía.

8th Latin American Conference on Biomedical Engineering and 42nd National Conference on Biomedical Engineering - Proceedings of CLAIB-CNIB 2019. ed. / César A. González Díaz; Christian Chapa González; Eric Laciar Leber; Hugo A. Vélez; Norma P. Puente; Dora-Luz Flores; Adriano O. Andrade; Héctor A. Galván; Fabiola Martínez; Renato García; Citlalli J. Trujillo; Aldo R. Mejía. Springer, 2020. p. 315-321 (IFMBE Proceedings; Vol. 75).

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferencia

TY - GEN

T1 - An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data

AU - Ariza-Jiménez, Leandro

AU - Pinel, Nicolás

AU - Villa, Luisa F.

AU - Quintero, Olga Lucía

PY - 2020/1/1

Y1 - 2020/1/1

N2 - 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.

AB - 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.

KW - Biological data

KW - Clustering

KW - Entropy

KW - Graph

KW - Metagenomic binning

KW - Spike sorting

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M3 - Contribución a la conferencia

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SN - 9783030306472

T3 - IFMBE Proceedings

SP - 315

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BT - 8th Latin American Conference on Biomedical Engineering and 42nd National Conference on Biomedical Engineering - Proceedings of CLAIB-CNIB 2019

A2 - González Díaz, César A.

A2 - Chapa González, Christian

A2 - Laciar Leber, Eric

A2 - Vélez, Hugo A.

A2 - Puente, Norma P.

A2 - Flores, Dora-Luz

A2 - Andrade, Adriano O.

A2 - Galván, Héctor A.

A2 - Martínez, Fabiola

A2 - García, Renato

A2 - Trujillo, Citlalli J.

A2 - Mejía, Aldo R.

PB - Springer

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Ariza-Jiménez L, Pinel N, Villa LF, Quintero OL. An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data. En González Díaz CA, Chapa González C, Laciar Leber E, Vélez HA, Puente NP, Flores D-L, Andrade AO, Galván HA, Martínez F, García R, Trujillo CJ, Mejía AR, editores, 8th Latin American Conference on Biomedical Engineering and 42nd National Conference on Biomedical Engineering - Proceedings of CLAIB-CNIB 2019. Springer. 2020. p. 315-321. (IFMBE Proceedings). https://doi.org/10.1007/978-3-030-30648-9_41