Frequency Modulated Möbius Model Accurately Predicts Rhythmic Signals in Biological and Physical Sciences
| Autor: | Cristina Rueda, Shyamal D. Peddada, Yolanda Larriba |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Multidisciplinary Computer science lcsh:R lcsh:Medicine Astronomy and planetary science Measure (mathematics) Expression (mathematics) Article Computational biology and bioinformatics 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Rhythm Parametric model lcsh:Q lcsh:Science Algorithm 030217 neurology & neurosurgery |
| Zdroj: | Scientific Reports, Vol 9, Iss 1, Pp 1-10 (2019) Scientific Reports |
| ISSN: | 2045-2322 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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