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Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

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Published on: August 8, 2019

PROTECT: Protein circadian time prediction using unsupervised learning.

Aram Ansary Ogholbake¹, Qiang Cheng¹

¹Department of Internal Medicine and Department of Computer Science, University of Kentucky, Lexington, KY, USA.

|October 23, 2025

Summary

This summary is machine-generated.

Circadian rhythm disruption is linked to Alzheimer's disease (AD). A new deep learning method, PROTECT, analyzes proteomic data without time labels to reveal significant circadian changes in AD patients.

Keywords:

Biocomputational method Neural networks Protein Proteomics

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Area of Science:

Chronobiology
Neuroscience
Proteomics
Artificial Intelligence

Background:

Circadian rhythms govern human physiology, and their disruption is implicated in diseases such as Alzheimer's disease (AD).
Proteomic datasets often lack crucial time-label information, presenting significant challenges (small samples, high dimensionality, noise) for circadian rhythm analysis.

Purpose of the Study:

To introduce PROTECT, an unsupervised deep learning method for predicting circadian sample phases from unlabeled proteomic data.
To apply PROTECT to identify circadian disruptions in Alzheimer's disease (AD) using postmortem human brain and urine proteomic data.
To compare circadian patterns between AD and control subjects to understand AD-related circadian dysregulation.

Main Methods:

Developed PROTECT, an unsupervised deep learning algorithm utilizing greedy layer-wise pre-training and cosine-based fine-tuning.
Validated PROTECT's accuracy on existing time-labeled proteomic datasets.
Applied PROTECT to unlabeled human proteomic data from postmortem brain regions and urine samples.

Main Results:

PROTECT accurately predicts circadian sample phases without requiring time labels or prior knowledge of rhythmic proteins.
Analysis of AD proteomic data revealed significant circadian disruptions, identifying proteins with retained, lost, or altered rhythmicity.
Proteins maintaining rhythmicity exhibited region-specific phase shifts and amplitude changes; enrichment analysis provided functional insights into altered rhythmicity.

Conclusions:

PROTECT is an effective tool for circadian analysis of unlabeled proteomic data, overcoming common dataset limitations.
The study provides systematic evidence of circadian pattern alterations in Alzheimer's disease (AD) brain and urine proteomic data.
Findings highlight key proteins and functional pathways involved in AD-related circadian dysregulation, offering potential therapeutic targets.