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

ABSTRACT:

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 in cancer prevention, diagnosis, and treatment by substantially improving our understanding of underlying mechanisms as well as the discovery of novel biomarkers for different types of cancers. However, such analyses present a number of challenges, including heterogeneity, and high-dimensionality of omics data.

Methods

We propose a novel framework for multi-omics data

SUBMITTER: El-Manzalawy Y 

PROVIDER: S-EPMC6157248 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature