Predicting drug-induced transcriptome responses of a wide range of human cell lines by a novel tensor-train decomposition algorithm.

ABSTRACT:

Motivation

Genome-wide identification of the transcriptomic responses of human cell lines to drug treatments is a challenging issue in medical and pharmaceutical research. However, drug-induced gene expression profiles are largely unknown and unobserved for all combinations of drugs and human cell lines, which is a serious obstacle in practical applications.

Results

Here, we developed a novel computational method to predict unknown parts of drug-induced gene expression profiles for various human cell lines and predict new drug therapeutic indications for a wide range of diseases. We proposed a tensor-train weighted optimization (TT-WOPT) algorithm to predict the potential values for unknown parts in tensor-structured gene expression data. Our results revealed that the propos

SUBMITTER: Iwata M 

PROVIDER: S-EPMC6612872 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature