Predicting which genes will respond to transcription factor perturbations.

ABSTRACT: The ability to predict which genes will respond to the perturbation of a transcription factor serves as a benchmark for our systems-level understanding of transcriptional regulatory networks. In previous work, machine learning models have been trained to predict static gene expression levels in a biological sample by using data from the same or similar samples, including data on their transcription factor binding locations, histone marks, or DNA sequence. We report on a different challenge-training machine learning models to predict which genes will respond to the perturbation of a transcription factor without using any data from the perturbed cells. We find that existing transcription factor location data (ChIP-seq) from human cells have very little detectable utility for predicting which

SUBMITTER: Kang Y 

PROVIDER: S-EPMC9339286 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature