期刊
出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.2103070118
关键词
explainable AI circadian transcriptome regulation function
资金
- Science and Technology Facilities Council Hartree Centre's Innovation Return on Research program - Department for Business, Energy, and Industrial Strategy
Machine learning is used to predict complex circadian gene expression patterns in Arabidopsis, classifying genes and revealing potential regulatory mechanisms without experimental work or prior knowledge. Model interpretation helps optimize sampling strategies and accurately predict circadian time, providing insight into biological processes and experimental design.
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 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 time point, deriving marker transcripts that are most impactful for accurate prediction; this could facilitate the identification of altered clock function from existing datasets.
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