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

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

doi:10.1007/978-3-030-30648-9_41

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

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

ARKADIUS Research Group

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	8th Latin American Conference on Biomedical Engineering and 42nd National Conference on Biomedical Engineering - Proceedings of CLAIB-CNIB 2019
Editors	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
Publisher	Springer
Pages	315-321
Number of pages	7
ISBN (Print)	9783030306472
DOIs	https://doi.org/10.1007/978-3-030-30648-9_41
State	Published - 1 Jan 2020
Event	8th Latin American Conference on Biomedical Engineering and the 42nd National Conference on Biomedical Engineering, CLAIB-CNIB 2019 - Cancún, Mexico Duration: 2 Oct 2019 → 5 Oct 2019

Publication series

Name	IFMBE Proceedings
Volume	75
ISSN (Print)	1680-0737
ISSN (Electronic)	1433-9277

Conference

Conference	8th Latin American Conference on Biomedical Engineering and the 42nd National Conference on Biomedical Engineering, CLAIB-CNIB 2019
Country/Territory	Mexico
City	Cancún
Period	2/10/19 → 5/10/19

Keywords

Biological data
Clustering
Entropy
Graph
Metagenomic binning
Spike sorting

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10.1007/978-3-030-30648-9_41

Cite this

Ariza-Jiménez, L., Pinel, N., Villa, L. F., & Quintero, O. L. (2020). An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data. In 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. et al. / 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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title = "An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data",

abstract = "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.",

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author = "Leandro Ariza-Jim{\'e}nez and Nicol{\'a}s Pinel and Villa, {Luisa F.} and Quintero, {Olga Luc{\'i}a}",

note = "Funding Information: Acknowledgments. This research was supported by Centro de Excelencia y Apropiaci{\'o}n en Big Data y Data Analytics -Alianza CAOBA-and Universidad EAFIT. Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; null ; Conference date: 02-10-2019 Through 05-10-2019",

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editor = "{Gonz{\'a}lez D{\'i}az}, {C{\'e}sar A.} and {Chapa Gonz{\'a}lez}, Christian and {Laciar Leber}, Eric and V{\'e}lez, {Hugo A.} and Puente, {Norma P.} and Dora-Luz Flores and Andrade, {Adriano O.} and Galv{\'a}n, {H{\'e}ctor A.} and Fabiola Mart{\'i}nez and Renato Garc{\'i}a and Trujillo, {Citlalli J.} and Mej{\'i}a, {Aldo R.}",

booktitle = "8th Latin American Conference on Biomedical Engineering and 42nd National Conference on Biomedical Engineering - Proceedings of CLAIB-CNIB 2019",

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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. in 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, Mexico, 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. et al.

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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

N1 - Funding Information: Acknowledgments. This research was supported by Centro de Excelencia y Apropiación en Big Data y Data Analytics -Alianza CAOBA-and Universidad EAFIT. Publisher Copyright: © 2020, Springer Nature Switzerland AG.

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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

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KW - Metagenomic binning

KW - Spike sorting

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M3 - Contribución a la conferencia

AN - SCOPUS:85075700831

SN - 9783030306472

T3 - IFMBE Proceedings

SP - 315

EP - 321

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

Y2 - 2 October 2019 through 5 October 2019

ER -

Ariza-Jiménez L, Pinel N, Villa LF, Quintero OL. An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data. In 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, editors, 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). doi: 10.1007/978-3-030-30648-9_41

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

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Keywords

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