Sparse group penalized integrative analysis of multiple cancer prognosis datasets.

ABSTRACT: In cancer research, high-throughput profiling studies have been extensively conducted, searching for markers associated with prognosis. Owing to the 'large d, small n' characteristic, results generated from the analysis of a single dataset can be unsatisfactory. Recent studies have shown that integrative analysis, which simultaneously analyses multiple datasets, can be more effective than single-dataset analysis and classic meta-analysis. In most of existing integrative analysis, the homogeneity model has been assumed, which postulates that different datasets share the same set of markers. Several approaches have been designed to reinforce this assumption. In practice, different datasets may differ in terms of patient selection criteria, profiling techniques, and many other aspects. Such d

SUBMITTER: Liu J 

PROVIDER: S-EPMC4090387 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature