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

Laura-Jayne Gardiner, Rachel Rusholme-Pilcher, Josh Colmer, Hannah Rees, Juan Manuel Crescente, Anna Paola Carrieri, Susan Duncan, Edward O. Pyzer-Knapp, Ritesh Krishna, Anthony Hall

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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 methodswith 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.

Original languageEnglish
Article numbere2103070118
JournalProceedings of the National Academy of Sciences of the United States of America (PNAS)
Volume118
Issue number32
Early online date5 Aug 2021
DOIs
Publication statusPublished - 10 Aug 2021

Keywords

  • Circadian
  • Explainable AI
  • Function
  • Regulation
  • Transcriptome

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