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National Academy of Sciences, Proceedings of the National Academy of Sciences, 32(118), 2021

DOI: 10.1073/pnas.2103070118

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

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Abstract

Significance The circadian clock is an internal molecular 24-h timer that is critical to life on Earth. We describe a series of artificial intelligence (AI)– and machine learning (ML)–based approaches that enable more cost-effective analysis and insight into circadian regulation and function. Throughout the manuscript, we illuminate what is inside the ML “black box” via explanation or interpretation of predictive ML models. Using this interpretation of our models, we derive biological insights into why a prediction was made, alongside accurate predictions. Most innovatively, we use only DNA sequence features for accurate circadian gene expression prediction. Using explainable AI, we define possible, responsible regulatory elements as we make these predictions; this critically requires no prior knowledge of regulatory elements.