Bayesian detection of periodic mRNA time profiles without use of training examples

Background: Detection of periodically expressed genes from microarray data without use of known periodic and non-periodic training examples is an important problem, e.g. for identifying genes regulated by the cell-cycle in poorly characterised organisms. Commonly the investigator is only interested in genes expressed at a particular frequency that characterizes the process under study but this frequency is seldom exactly known. Previously proposed detector designs require access to labelled training examples and do not allow systematic incorporation of diffuse prior knowledge available about the period time. Results: A learning-free Bayesian detector that does not rely on labelled training examples and allows incorporation of prior knowledge about the period time is introduced. It is shown to outperform two recently proposed alternative learning-free detectors on simulated data generated with models that are different from the one used for detector design. Results from applying the de...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Claes R. Andersson, Anders Isaksson, Mats G. Gustafsson
• Keywords: Alternative Learning-free Detectors, BMCBI 2006, Detector, Period Time