PathME: pathway based multi-modal sparse autoencoders for clustering of patient-level multi-omics data.

ABSTRACT: BACKGROUND:Recent years have witnessed an increasing interest in multi-omics data, because these data allow for better understanding complex diseases such as cancer on a molecular system level. In addition, multi-omics data increase the chance to robustly identify molecular patient sub-groups and hence open the door towards a better personalized treatment of diseases. Several methods have been proposed for unsupervised clustering of multi-omics data. However, a number of challenges remain, such as the magnitude of features and the large difference in dimensionality across different omics data sources. RESULTS:We propose a multi-modal sparse denoising autoencoder framework coupled with sparse non-negative matrix factorization to robustly cluster patients based on multi-omics data. The propo

SUBMITTER: Lemsara A 

PROVIDER: S-EPMC7161108 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature