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:
-
Ariza-Jiménez L.
Pinel N.
Villa L.F.
Quintero O.L.
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad EAFIT
- Repositorio:
- Repositorio EAFIT
- Idioma:
- eng
- OAI Identifier:
- oai:repository.eafit.edu.co:10784/26750
- Acceso en línea:
- https://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=9841
http://hdl.handle.net/10784/26750
- Palabra clave:
- Biomedical
engineering
Biophysics
Entropy
Graphic
methods
Machine
learning
Unsupervised
learning
Biological
data
Clustering
Graph
Metagenomic
binning
Spike-sorting
Sorting
- Rights
- License
- https://v2.sherpa.ac.uk/id/publication/issn/1680-0737