Feature weight estimation for gene selection: a local hyperlinear learning approach

BACKGROUND: Modeling high-dimensional data involving thousands of variables is particularly important for gene expression profiling experiments, nevertheless,it remains a challenging task. One of the challenges is to implement an effective method for selecting a small set of relevant genes, buried i...

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Detalles Bibliográficos
Autores principales: Cai, Hongmin, Ruan, Peiying, Ng, Michael, Akutsu, Tatsuya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Methodology Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4007530/
https://www.ncbi.nlm.nih.gov/pubmed/24625071
http://dx.doi.org/10.1186/1471-2105-15-70

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4007530/
https://www.ncbi.nlm.nih.gov/pubmed/24625071
http://dx.doi.org/10.1186/1471-2105-15-70

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