Finding Significantly Expressed genes from time-course expression profiles

This paper proposes a statistical method for finding Significantly Expressed (SE) genes from time-course expression. SE genes are time-dependent while non-SE genes are time-independent. This method models time-dependent gene expression profiles by autoregressive equations plus Gaussian noises, and time-independent ones by Gaussian noises. The statistical F-testing is used to calculate the probability (p-value) that a profile is time-independent. Both a synthetic dataset and a biological dataset were employed to evaluate the performance of this method, measured by the False Discovery Rate (FDR) and the False Non-discovery Rate (FNR). Results show that the proposed method outperforms traditional methods.

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