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
| 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. |
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