MVDA: a multi-view genomic data integration methodology.

ABSTRACT:

Background

Multiple high-throughput molecular profiling by omics technologies can be collected for the same individuals. Combining these data, rather than exploiting them separately, can significantly increase the power of clinically relevant patients subclassifications.

Results

We propose a multi-view approach in which the information from different data layers (views) is integrated at the levels of the results of each single view clustering iterations. It works by factorizing the membership matrices in a late integration manner. We evaluated the effectiveness and the performance of our method on six multi-view cancer datasets. In all the cases, we found patient sub-classes with statistical significance, identifying novel sub-groups previously not emphasized in literature.

SUBMITTER: Serra A 

PROVIDER: S-EPMC4539887 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature