Min-redundancy and max-relevance multi-view feature selection for predicting ovarian cancer survival using multi-omics data

BACKGROUND: Large-scale collaborative precision medicine initiatives (e.g., The Cancer Genome Atlas (TCGA)) are yielding rich multi-omics data. Integrative analyses of the resulting multi-omics data, such as somatic mutation, copy number alteration (CNA), DNA methylation, miRNA, gene expression, and...

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Detalles Bibliográficos
Autores principales: EL-Manzalawy, Yasser, Hsieh, Tsung-Yu, Shivakumar, Manu, Kim, Dokyoon, Honavar, Vasant
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6157248/
https://www.ncbi.nlm.nih.gov/pubmed/30255801
http://dx.doi.org/10.1186/s12920-018-0388-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6157248/
https://www.ncbi.nlm.nih.gov/pubmed/30255801
http://dx.doi.org/10.1186/s12920-018-0388-0

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