Inferring perturbation profiles of cancer samples.

ABSTRACT:

Motivation

Cancer is one of the most prevalent diseases in the world. Tumors arise due to important genes changing their activity, e.g. when inhibited or over-expressed. But these gene perturbations are difficult to observe directly. Molecular profiles of tumors can provide indirect evidence of gene perturbations. However, inferring perturbation profiles from molecular alterations is challenging due to error-prone molecular measurements and incomplete coverage of all possible molecular causes of gene perturbations.

Results

We have developed a novel mathematical method to analyze cancer driver genes and their patient-specific perturbation profiles. We combine genetic aberrations with gene expression data in a causal network derived across patients to infer unobserved perturba

SUBMITTER: Pirkl M 

PROVIDER: S-EPMC8388028 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature