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