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

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 express...

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Bibliographic Details
Main Authors: Yasser EL-Manzalawy, Tsung-Yu Hsieh, Manu Shivakumar, Dokyoon Kim, Vasant Honavar
Format: Article
Language:English
Published: BMC 2018-09-01
Series:BMC Medical Genomics
Subjects:
Multi-omics data integration
Multi-view feature selection
Cancer survival prediction
Machine learning
Online Access:http://link.springer.com/article/10.1186/s12920-018-0388-0

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