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USE OF APRIORI KNOWLEDGE ON DYNAMIC BAYESIAN MODELS IN TIME-COURSE EXPRESSION DATA PREDICTION
[article]
2012
Bayesian networks, one of the most widely used techniques to understand or predict the future by making use of current or previous data, have gained credence over the last decade for their ability to simulate large gene expression datasets to track and predict the reasons for changes in biological systems. In this work, we present a dynamic Bayesian model with gene annotation scores such as the gene characterization index (GCI) and the GenCards inferred functionality score (GIFtS) to understand
doi:10.7912/c2/893
fatcat:7bw54knpkzdl3del6pyyn5z2nu