Learning Parsimonious Classification Rules from Gene Expression Data Using Bayesian Networks with Local Structure

The comprehensibility of good predictive models learned from high-dimensional gene expression data is attractive because it can lead to biomarker discovery. Several good classifiers provide comparable predictive performance but differ in their abilities to summarize the observed data. We extend a Ba...

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Detalles Bibliográficos
Autores principales: Lustgarten, Jonathan Lyle, Balasubramanian, Jeya Balaji, Visweswaran, Shyam, Gopalakrishnan, Vanathi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5358670/
https://www.ncbi.nlm.nih.gov/pubmed/28331847
http://dx.doi.org/10.3390/data2010005

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5358670/
https://www.ncbi.nlm.nih.gov/pubmed/28331847
http://dx.doi.org/10.3390/data2010005

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