Predicting gene knockout effects from expression data.

ABSTRACT:

Background

The study of gene essentiality, which measures the importance of a gene for cell division and survival, is used for the identification of cancer drug targets and understanding of tissue-specific manifestation of genetic conditions. In this work, we analyze essentiality and gene expression data from over 900 cancer lines from the DepMap project to create predictive models of gene essentiality.

Methods

We developed machine learning algorithms to identify those genes whose essentiality levels are explained by the expression of a small set of "modifier genes". To identify these gene sets, we developed an ensemble of statistical tests capturing linear and non-linear dependencies. We trained several regression models predicting the essentiality of each target gene, and

SUBMITTER: Rosenski J 

PROVIDER: S-EPMC9938619 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature