Background: We investigate whether annotation of gene function can be improved using a classification scheme that is aware that functional classes are organized in a hierarchy. The classifiers look at phylogenic descriptors, sequence based attributes, and predicted secondary structure. We discuss three Bayesian models and compare their performance in terms of predictive accuracy. These models are the ordinary multinomial logit (MNL) model, a hierarchical model based on a set of nested MNL models, and an MNL model with a prior that introduces correlations between the parameters for classes that are nearby in the hierarchy. We also provide a new scheme for combining different sources of information. We use these models to predict the functional class of Open Reading Frames (ORFs) from the E. coli genome.
A Bayesian model consisting of a set of nested multinomial logit models, where a prior describing correlations of parameters for nearby GO terms is trained by the hierarchy, has been proposed in REF .
Gene function classification using Bayesian models with hierarchy-based priors
BMC Bioinformatics
BMC Bioinformatics
Biology Computer Science Medicine