Discovery of genes positively modulating treatment effect using potential outcome framework and Bayesian update.

ABSTRACT:

Background

The recent explosion of cancer genomics provides extensive information about mutations and gene expression changes in cancer. However, most of the identified gene mutations are not clinically utilized. It remains uncertain whether the presence of a certain genetic alteration will affect treatment response. Conventional statistics have limitations for causal inferences and are hard to gain sufficient power in genomic datasets. Here, we developed and evaluated a C-search algorithm for searching the causal genes that maximize the effect of the treatment.

Methods

The algorithm was developed based on the potential outcome framework and Bayesian posterior update. The precision of the algorithm was validated using a simulation dataset. The algorithm was implemented to a

SUBMITTER: Lee Y 

PROVIDER: S-EPMC9047392 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature