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

Gardiner, Laura-Jayne, Rusholme-Pilcher, Rachel, Colmer, Josh, Rees, Hannah, Crescente, Juan Manuel, Carrieri, Anna Paola, Duncan, Susan, Pyzer-Knapp, Edward O ORCID: 0000-0002-8232-8282, Krishna, Ritesh and Hall, Anthony (2021) Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 118 (32). e2103070118-.

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Official URL: http://dx.doi.org/10.1073/pnas.2103070118

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 <i>Arabidopsis</i> 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 class. Model interpretation/explanation provides the backbone of our methodological advances, giving insight into biological processes and experimental design. Next, we use model interpretation to optimize sampling strategies when we predict circadian transcripts using reduced numbers of transcriptomic timepoints. Finally, we predict the circadian time from a single, transcriptomic timepoint, deriving marker transcripts that are most impactful for accurate prediction; this could facilitate the identification of altered clock function from existing datasets.

Item Type:	Article
Uncontrolled Keywords:	explainable AI&nbsp, circadian&nbsp, transcriptome&nbsp, regulation&nbsp, function
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	23 Dec 2021 16:00
Last Modified:	10 Feb 2024 03:29
DOI:	10.1073/pnas.2103070118
Open Access URL:	https://www.pnas.org/content/118/32/e2103070118
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3145985