High dimensional model representation of log-likelihood ratio: binary classification with expression data

BACKGROUND: Binary classification rules based on a small-sample of high-dimensional data (for instance, gene expression data) are ubiquitous in modern bioinformatics. Constructing such classifiers is challenging due to (a) the complex nature of underlying biological traits, such as gene interactions...

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Detalles Bibliográficos
Autores principales: Foroughi pour, Ali, Pietrzak, Maciej, Dalton, Lori A, Rempała, Grzegorz A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Methodology Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7183128/
https://www.ncbi.nlm.nih.gov/pubmed/32334509
http://dx.doi.org/10.1186/s12859-020-3486-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7183128/
https://www.ncbi.nlm.nih.gov/pubmed/32334509
http://dx.doi.org/10.1186/s12859-020-3486-x

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