GIFT: Guided and Interpretable Factorization for Tensors with an application to large-scale multi-platform cancer analysis.

ABSTRACT:

Motivation

Given multi-platform genome data with prior knowledge of functional gene sets, how can we extract interpretable latent relationships between patients and genes? More specifically, how can we devise a tensor factorization method which produces an interpretable gene factor matrix based on functional gene set information while maintaining the decomposition quality and speed?

Results

We propose GIFT, a Guided and Interpretable Factorization for Tensors. GIFT provides interpretable factor matrices by encoding prior knowledge as a regularization term in its objective function. We apply GIFT to the PanCan12 dataset (TCGA multi-platform genome data) and compare the performance with P-Tucker, our baseline method without prior knowledge constraint, and Silenced-TF, our naiv

SUBMITTER: Lee J 

PROVIDER: S-EPMC6289137 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature