Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Bioinformatics Research and Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
