ArticleScientific reports2019
Frequency Modulated Möbius Model Accurately Predicts Rhythmic Signals in Biological and Physical Sciences.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 31 citations in OpenAlex.
- In silico models in oncology, neurology, and epidemiology: systems-level and multiscale perspectives.NPJ systems biology and applications · 2026Review
- Precise peak width estimation for solving key challenges in biosignal and spectral analysis.Scientific reports · 2026Article
- Physiologically Guided Modeling for EEG Multichannel Signals.Bioengineering (Basel, Switzerland) · 2026Article
- Cell modeling using frequency modulation.PloS one · 2024Article
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- CIRCUST: A novel methodology for temporal order reconstruction of molecular rhythms; validation and application towards a daily rhythm gene expression atlas in humans.PLoS computational biology · 2023Article
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- Real-time imaging of asthmatic epithelial cells identifies migratory deficiencies under type-2 conditions.The Journal of allergy and clinical immunology · 2022Article
- The hidden waves in the ECG uncovered revealing a sound automated interpretation method.Scientific reports · 2021Article
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- Electrophysiological and Transcriptomic Features Reveal a Circular Taxonomy of Cortical Neurons.Frontiers in human neuroscience · 2021Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivated by applications in physical and biological sciences, we developed a Frequency Modulated Möbius (FMM) model to describe rhythmic patterns in oscillatory systems. Unlike standard symmetric sinusoidal models, FMM is a flexible parametric model that allows deformations to sinusoidal shape to accommodate commonly seen asymmetries in applications. FMM model parameters are easy to estimate and the model is easy to interpret complex rhythmic data. We illustrate FMM model in three disparate applications, namely, circadian clock gene expression, corticoptropin levels in depressed patients and the temporal light intensity patterns of distant stars. In each case, FMM model is demonstrated to be flexible, scientifically plausible and easy to interpret. Analysis of synthetic data derived from patterns of real data, suggest that FMM model fits the data very well both visually as well as in terms of the goodness of fit measure total mean squared error. An R language based software for implementing FMM model is available.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.