Dynamic-model-based method for selecting significantly expressed genes from time-course expression profiles

Authors:
Fang-Xiang Wu;Wen-Jun Zhang
Affiliations:
Department of Mechanical Engineering, Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, Canada;Department of Mechanical Engineering, Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, Canada
Venue:
IEEE Transactions on Information Technology in Biomedicine
Year:
2010

Citing 5
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Abstract

This paper proposes a dynamic-model-based method for selecting significantly expressed (SE) genes from their time-course expression profiles. A gene is considered to be SE if its time-course expression profile is more likely time-dependent than random. The proposed method describes a time-dependent gene expression profile by a nonzero-order autoregressive (AR) model, and a time-independent gene expression profile by a zero-order AR model. Akaike information criterion (AIC) is used to compare themodels and subsequently determinewhether a time-course gene expression profile is time-independent or time-dependent. The performance of the proposedmethod is investigated on both a synthetic dataset and a real-life biological dataset in terms of the false discovery rate (FDR) and the false nondiscovery rate (FNR). The results show that the proposed method is valid for selecting SE genes from their time-course expression profiles.