Dcpr: A Deep Learning Framework for Circadian Phase Reconstruction

Authors

Xiao Han
Xiaochen Cen
Zhijin Li
Xiaobo Zhou
Zhiwei Ji

Publication Date

12-30-2025

Journal

BMC Bioinformatics

DOI

10.1186/s12859-025-06363-2

PMID

41469552

PMCID

PMC12866578

PubMedCentral® Posted Date

12-30-2025

PubMedCentral® Full Text Version

Post-print

Abstract

Background: The circadian clock is an evolutionarily conserved system that orchestrates 24-h physiological rhythms through transcriptional and translational feedback loops. Mounting evidence suggests a bidirectional relationship between circadian rhythm alteration and disease progression, positioning the circadian clock as a potential therapeutic target. Due to the scarcity of high-resolution temporal omics data, it remains very challenging to elucidate the underlying regulatory mechanisms of the circadian system. As a practical alternative, public untimed transcriptomic datasets offer the potential to infer gene expression oscillations retrospectively. However, existing computational approaches for circadian phase estimation often suffer from limited predictive accuracy, reducing their ability to reliably reconstruct rhythmic gene expression patterns.

Results: To overcome these limitations, we develop DCPR, an unsupervised deep learning framework designed to accurately reconstruct the circadian phase from untimed transcriptomic data. Through comprehensive analyses of both simulated and real data, DCPR consistently overperforms existing methods in circadian phase estimation. Additional validations using knowledgebase mining and ex vivo experimental data further support DCPR's efficacy in reconstructing the oscillatory pattern of gene expression and detecting circadian variation.

Conclusions: Our study demonstrates that DCPR is a highly versatile tool for systematically identifying transcriptional rhythms from untimed expression data. This tool will facilitate therapeutics discovery for circadian-related behavioral and pathological disorders.

Keywords

Deep Learning, Circadian Rhythm, Circadian Clocks, Humans, Computational Biology, Animals, Transcriptome, Gene Expression Profiling, Circadian rhythm, Gene expression, Circadian variation, Phase reconstruction, Alzheimer’s disease

Published Open-Access

yes

Recommended Citation

Han, Xiao; Cen, Xiaochen; Li, Zhijin; et al., "Dcpr: A Deep Learning Framework for Circadian Phase Reconstruction" (2025). Faculty, Staff and Student Publications. 807.
https://digitalcommons.library.tmc.edu/uthshis_docs/807