Use of Bayesian networks to probabilistically model and improve the likelihood of validation of microarray findings by RT-PCR

Sangeeta B. English, Shou-Ching Shih, Marco F. Ramoni, Lois E. Smith, Atul J. Butte
2009 Journal of Biomedical Informatics  
Though genome-wide technologies, such as microarrays, are widely used, data from these methods are considered noisy; there is still varied success in downstream biological validation. We report a method that increases the likelihood of successfully validating microarray findings using real time RT-PCR, including genes at low expression levels and with small differences. We use a Bayesian network to identify the most relevant sources of noise based on the successes and failures in validation for
more » ... an initial set of selected genes, and then improve our subsequent selection of genes for validation based on eliminating these sources of noise. The network displays the significant sources of noise in an experiment, and scores the likelihood of validation for every gene. We show how the method can significantly increase validation success rates. In conclusion, in this study, we have successfully added a new automated step to determine the contributory sources of noise that determine successful or unsuccessful downstream biological validation.
doi:10.1016/j.jbi.2008.08.009 pmid:18790084 pmcid:PMC3962641 fatcat:hzud4lzapza7rpcrznsf7eqvni