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BMCBI
2005

Robust detection of periodic time series measured from biological systems

10 years 2 months ago
Robust detection of periodic time series measured from biological systems
Background: Periodic phenomena are widespread in biology. The problem of finding periodicity in biological time series can be viewed as a multiple hypothesis testing of the spectral content of a given time series. The exact noise characteristics are unknown in many bioinformatics applications. Furthermore, the observed time series can exhibit other non-idealities, such as outliers, short length and distortion from the original wave form. Hence, the computational methods should preferably be robust against such anomalies in the data. Results: We propose a general-purpose robust testing procedure for finding periodic sequences in multiple time series data. The proposed method is based on a robust spectral estimator which is incorporated into the hypothesis testing framework using a so-called g-statistic together with correction for multiple testing. This results in a robust testing procedure which is insensitive to heavy contamination of outliers, missing-values, short time series, nonl...
Miika Ahdesmäki, Harri Lähdesmäki,
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2005
Where BMCBI
Authors Miika Ahdesmäki, Harri Lähdesmäki, Ronald K. Pearson, Heikki Huttunen, Olli Yli-Harja
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