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• W2124014908 abstract "The microarray technique allows the simultaneous measurements of the expression levels of thousands of mRNAs. By mining these data one can identify the dynamics of the gene expression time series. The detection of genes that are periodically expressed is an important step that allows us to study the regulatory mechanisms associated with the circadian cycle. The problem of finding periodicity in biological time series poses many challenges. Such challenge occurs due to the fact that the observed time series usually exhibit non-idealities, such as noise, short length, outliers and unevenly sampled time points. Consequently, the method for finding periodicity should preferably be robust against such anomalies in the data. In this paper, we propose a general and robust procedure for identifying genes with a periodic signature at a given significance level. This identification method is based on autoregressive models and the information theory. By using simulated data we show that the suggested method is capable of identifying rhythmic profiles even in the presence of noise and when the number of data points is small. By recourse of our analysis, we uncover the circadian rhythmic patterns underlying the gene expression profiles from Cyanobacterium Synechocystis." @default.
• W2124014908 created "2016-06-24" @default.
• W2124014908 creator A5083474100 @default.
• W2124014908 creator A5089833390 @default.
• W2124014908 date "2011-10-18" @default.
• W2124014908 modified "2023-10-16" @default.
• W2124014908 title "Time-Course Analysis of Cyanobacterium Transcriptome: Detecting Oscillatory Genes" @default.
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• W2124014908 doi "https://doi.org/10.1371/journal.pone.0026291" @default.
• W2124014908 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3196541" @default.
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• W2124014908 hasPublicationYear "2011" @default.
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