title: Application of Bayesian logistic regression to mining biomedical data.
abstract: Mining high dimensional biomedical data with existing classifiers is challenging and the predictions are often inaccurate. We investigated the use of Bayesian Logistic Regression (B-LR) for mining such data to predict and classify various disease conditions. The analysis was done on twelve biomedical datasets with binary class variables and the performance of B-LR was compared to those from other popular classifiers on these datasets with 10-fold cross validation using the WEKA data mining toolkit. The statistical significance of the results was analyzed by paired two tailed t-tests and non-parametric Wilcoxon signed-rank tests. We observed overall that B-LR with non-informative Gaussian priors performed on par with other classifiers in terms of accuracy, balanced accuracy and AUC. These results suggest that it is worthwhile to explore the application of B-LR to predictive modeling tasks in bioinformatics using informative biological prior probabilities. With informative prior probabilities, we conjecture that the performance of B-LR will improve.
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA.
authors.forename: Viji R
authors.initials: VR
authors.lastname: Avali
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA ; Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA ; Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA.
authors.forename: Gregory F
authors.initials: GF
authors.lastname: Cooper
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA ; Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA ; Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA.
authors.forename: Vanathi
authors.initials: V
authors.lastname: Gopalakrishnan
mesh_terms: D019540:Area Under Curve; D001499:Bayes Theorem; D019295:Computational Biology; D057225:Data Mining / Q000379:methods*; D004194:Disease / Q000145:classification*; D006801:Humans; D016015:Logistic Models; D018709:Statistics, Nonparametric