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Related Experiment Video

Updated: Jan 7, 2026

Analysis of Circadian Photoresponses in Drosophila Using Locomotor Activity

Analysis of Circadian Photoresponses in Drosophila Using Locomotor Activity

DCPR: a deep learning framework for circadian phase reconstruction.

Xiao Han^1,2, Xiaochen Cen^1,2, Zhijin Li³

¹College of Artificial Intelligence, Nanjing Agricultural University, No. 666 Binjiang Avenue, Nanjing, 211800, Jiangsu, China.

BMC Bioinformatics

|December 30, 2025

Summary

This summary is machine-generated.

We developed DCPR, a deep learning tool to accurately estimate circadian phase from untimed transcriptomic data. This method improves the reconstruction of gene expression rhythms for circadian biology research.

Keywords:

Alzheimer’s disease Circadian rhythm Circadian variation Gene expression Phase reconstruction

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Last Updated: Jan 7, 2026

Analysis of Circadian Photoresponses in Drosophila Using Locomotor Activity

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Single-cell Resolution Fluorescence Live Imaging of Drosophila Circadian Clocks in Larval Brain Culture

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Published on: January 19, 2018

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Published on: August 8, 2019

Area of Science:

Chronobiology
Computational Biology
Genomics

Background:

The circadian clock regulates 24-h physiological rhythms via feedback loops.
Circadian rhythm disruption is linked to disease, making the clock a therapeutic target.
Limited high-resolution temporal omics data hinders understanding of circadian mechanisms.

Purpose of the Study:

To develop an accurate computational method for inferring circadian phase from untimed transcriptomic data.
To overcome limitations of existing methods in predicting gene expression oscillations.

Main Methods:

Developed DCPR, an unsupervised deep learning framework.
Applied DCPR to simulated and real-world untimed transcriptomic datasets.
Validated DCPR using knowledgebase mining and ex vivo experimental data.

Main Results:

DCPR demonstrated superior performance in circadian phase estimation compared to existing methods.
The framework accurately reconstructed gene expression oscillatory patterns.
DCPR effectively detected circadian variation in transcriptomic data.

Conclusions:

DCPR is a versatile tool for identifying transcriptional rhythms in untimed expression data.
This tool aids in discovering therapeutics for circadian-related disorders.
Enables systematic analysis of circadian patterns without time-series data.