Document Type
Article
Abstract
Motivation: Circadian rhythms date back to the origins of life, are found in virtually every species and every cell, and play fundamental roles in functions ranging from metabolism to cognition. Modern high-throughput technologies allow the measurement of concentrations of transcripts, metabolites and other species along the circadian cycle creating novel computational challenges and opportunities, including the problems of inferring whether a given species oscillate in circadian fashion or not, and inferring the time at which a set of measurements was taken.
Results: We first curate several large synthetic and biological time series datasets containing labels for both periodic and aperiodic signals. We then use deep learning methods to develop and train BIO-CYCLE, a system to robustly estimate which signals are periodic in high-throughput circadian experiments, producing estimates of amplitudes, periods, phases, as well as several statistical significance measures. Using the curated data, BIO-CYCLE is compared to other approaches and shown to achieve state-of-the-art performance across multiple metrics. We then use deep learning methods to develop and train BIO-CLOCK to robustly estimate the time at which a particular single-time-point transcriptomic experiment was carried. In most cases, BIO-CLOCK can reliably predict time, within approximately 1 h, using the expression levels of only a small number of core clock genes. BIO-CLOCK is shown to work reasonably well across tissue types, and often with only small degradation across conditions. BIO-CLOCK is used to annotate most mouse experiments found in the GEO database with an inferred time stamp.
© 2024, Association for the Advancement of Artificial Intelligence
Digital Object Identifier (DOI)
Publication Info
Published in Bioinformatics, Volume 32, Issue 12, 2016, pages i8-i17.
Rights
VC The Author 2016. Published by Oxford University Press. i8 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
APA Citation
Agostinelli, F., Ceglia, N., Shahbaba, B., Sassone-Corsi, P., & Baldi, P. (2016). What time is it? Deep learning approaches for circadian rhythms. Bioinformatics, 32(12). https://doi.org/10.1093/bioinformatics/btw243