Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function.

ABSTRACT: The circadian clock is an important adaptation to life on Earth. Here, we use machine learning to predict complex, temporal, and circadian gene expression patterns in Arabidopsis Most significantly, we classify circadian genes using DNA sequence features generated de novo from public, genomic resources, facilitating downstream application of our methods with no experimental work or prior knowledge needed. We use local model explanation that is transcript specific to rank DNA sequence features, providing a detailed profile of the potential circadian regulatory mechanisms for each transcript. Furthermore, we can discriminate the temporal phase of transcript expression using the local, explanation-derived, and ranked DNA sequence features, revealing hidden subclasses within the circadian clas

SUBMITTER: Gardiner LJ 

PROVIDER: S-EPMC8364196 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature