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

Abstract excerpt

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 protein expression, offer tantalizing possibilities for realizing the promise and potential of precision medicine...

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