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Circadian Rhythm

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Machine learning-based prediction of restless legs syndrome using digital phenotypes from wearables and smartphone data.

Scientific reports
Restless legs syndrome (RLS) is a relatively common neurosensory disorder that causes an irresistible urge for leg movement. RLS causes sleep disturbances and reduced quality of life, but accurate diagnosis remains challenging owing to the reliance o...

Prediction of heart failure patients with distinct left ventricular ejection fraction levels using circadian ECG features and machine learning.

PloS one
Heart failure (HF) encompasses a diverse clinical spectrum, including instances of transient HF or HF with recovered ejection fraction, alongside persistent cases. This dynamic condition exhibits a growing prevalence and entails substantial healthcar...

Identifying novel circadian rhythm biomarkers for diagnosis and prognosis of melanoma by an integrated bioinformatics and machine learning approach.

Aging
Melanoma is a highly malignant skin tumor with poor prognosis. Circadian rhythm is closely related to melanoma pathogenesis. This study aimed to identify key circadian rhythm genes (CRGs) in melanoma and explore their potential as diagnostic and prog...

Measuring activity-rest rhythms under different acclimation periods in a marine fish using automatic deep learning-based video tracking.

Chronobiology international
Most organisms synchronize to an approximately 24-hour (circadian) rhythm. This study introduces a novel deep learning-powered video tracking method to assess the stability, fragmentation, robustness and synchronization of activity rhythms in . Exper...

Machine learning reveals the transcriptional regulatory network and circadian dynamics of PCC 7942.

Proceedings of the National Academy of Sciences of the United States of America
is an important cyanobacterium that serves as a versatile and robust model for studying circadian biology and photosynthetic metabolism. Its transcriptional regulatory network (TRN) is of fundamental interest, as it orchestrates the cell's adaptatio...

Machine Learning Models for Predicting 24-Hour Intraocular Pressure Changes: A Comparative Study.

Medical science monitor : international medical journal of experimental and clinical research
BACKGROUND Predicting 24-hour intraocular pressure (IOP) fluctuations is crucial for enhancing glaucoma management. Traditional methods of measuring 24-hour IOP fluctuations are complex and present certain limitations. The present study leverages mac...

Machine learning model for menstrual cycle phase classification and ovulation day detection based on sleeping heart rate under free-living conditions.

Computers in biology and medicine
The accurate classification of menstrual cycle phases and detection of ovulation is critical for women's health management, particularly in addressing infertility, alleviating premenstrual syndrome, and preventing hormone-related disorders. However, ...

CircaKB: a comprehensive knowledgebase of circadian genes across multiple species.

Nucleic acids research
Circadian rhythms, which are the natural cycles that dictate various physiological processes over a 24-h period, have been increasingly recognized as important in the management and treatment of various human diseases. However, the lack of sufficient...

Circadian rhythm modulation in heart rate variability as potential biomarkers for major depressive disorder: A machine learning approach.

Journal of psychiatric research
Major depressive disorder (MDD) is associated with reduced heart rate variability (HRV), but its link to circadian rhythm modulation (CRM) of HRV is unclear. Given that depression disrupts circadian rhythms, assessing HRV fluctuations may better capt...