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This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and the requirement for uniformly sampled data. Biological time series often deviate significantly from the requirements of optimality for Fourier transformation. In this paper we present an alternative approach based on Bayesian inference. We show the value of placing spectral analysis in the framework of Bayesian inference and demonstrate how model comparison can automate this procedure.<\/jats:p><\/jats:sec>","DOI":"10.1186\/1752-0509-5-97","type":"journal-article","created":{"date-parts":[[2011,6,24]],"date-time":"2011-06-24T18:19:34Z","timestamp":1308939574000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Automated Bayesian model development for frequency detection in biological time series"],"prefix":"10.1186","volume":"5","author":[{"given":"Emma","family":"Granqvist","sequence":"first","affiliation":[]},{"given":"Giles ED","family":"Oldroyd","sequence":"additional","affiliation":[]},{"given":"Richard J","family":"Morris","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2011,6,24]]},"reference":[{"key":"708_CR1","volume-title":"Biochemical Oscillations and Cellular Rhythms: The Molecular Bases of Periodic and Chaotic Behaviour","author":"A Goldbeter","year":"1997","unstructured":"Goldbeter A: Biochemical Oscillations and Cellular Rhythms: The Molecular Bases of Periodic and Chaotic Behaviour. 1997, Cambridge University Press"},{"key":"708_CR2","volume-title":"The Fourier transform and its applications","author":"RN Bracewell","year":"1978","unstructured":"Bracewell RN: The Fourier transform and its applications. 1978, New York: McGraw-Hill, 2","edition":"2"},{"key":"708_CR3","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511790423","volume-title":"Probability theory: the logic of science","author":"ET Jaynes","year":"2003","unstructured":"Jaynes ET, Bretthorst GL: Probability theory: the logic of science. 2003, Cambridge, UK: Cambridge University Press"},{"key":"708_CR4","volume-title":"Nature","author":"JW Gibbs","year":"1899","unstructured":"Gibbs JW: Fourier's Series. 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