1 research outputs found
Gene Function Classification Using Bayesian Models with Hierarchy-Based Priors
We investigate the application of hierarchical classification schemes to the
annotation of gene function based on several characteristics of protein
sequences including 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 a 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. The results from all three models show substantial
improvement over previous methods, which were based on the C5 algorithm. The
MNL model using a prior based on the hierarchy outperforms both the
non-hierarchical MNL model and the nested MNL model. In contrast to previous
attempts at combining these sources of information, our approach results in a
higher accuracy rate when compared to models that use each data source alone.
Together, these results show that gene function can be predicted with higher
accuracy than previously achieved, using Bayesian models that incorporate
suitable prior information