Gene Function Classification Using Bayesian Models with Hierarchy-Based Priors

Babak Shahbaba, Dept. of Public Health Sciences, University of Toronto
Radford M. Neal, Dept. of Statistics and Dept. of Computer Science, University of Toronto

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.
Results: The results from all three models show substantial improvement over previous methods, which were based on the C5 decision tree 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 the three sources of information in this dataset, our new approach to combining data sources produces a higher accuracy rate than applying our models to each data source alone.
Conclusion: Together, these results show that gene function can be predicted with higher accuracy than previously achieved, using Bayesian models that incorporate suitable prior information.

BMC Bioinformatics, 2006, 7:448, 9 pages: pdf, html.

Associated references: This is a revised version of the following technical report:
Shahbaba, B. and Neal, R. M. (2006) ``Gene function classification using Bayesian models with hierarchy-based priors'', Technical Report No. 0606, Dept. of Statistics, 14 pages: abstract, postscript, pdf, associated references.

The class of models that are used here for gene function classification was introduced in the following technical report:

Shahbaba, B. and Neal, R. M. (2005) ``Improving classification when a class hierarchy is available using a hierarchy-based prior'', Technical Report No. 0510, Dept. of Statistics, 11 pages: abstract, postscript, pdf, associated references.