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:
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
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License
http://purl.org/coar/access_right/c_16ec
Description
Summary: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.