Relevant and Non-Redundant Feature Selection for Cancer Classification and Subtype Detection

SIMPLE SUMMARY: Here we introduce a new feature selection algorithm DTA, which selects important, non-redundant, and relevant features from diverse omics data. DTA selects non-redundant features by maximizing the similarity between each patient pair by an approximate k-cover algorithm. We successful...

Descripción completa

Detalles Bibliográficos
Autores principales: Rana, Pratip, Thai, Phuc, Dinh, Thang, Ghosh, Preetam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8428340/
https://www.ncbi.nlm.nih.gov/pubmed/34503106
http://dx.doi.org/10.3390/cancers13174297

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8428340/
https://www.ncbi.nlm.nih.gov/pubmed/34503106
http://dx.doi.org/10.3390/cancers13174297

Ejemplares similares