A Bayesian method for biological pathway discovery from high-throughput experimental data

This poster describes a novel Bayesian method for discovering intra-cellular pathways from high throughput data. This Bayesian method is generalized from a deterministic algorithm [1], and it combines experimental data with prior belief to produce as output a probability distribution over the possible causal relationships between each pair of variables. We applied this algorithm to gene expression data [2] on galactose metabolism in yeast. The Area Under ROC curve (AUROC) for the Wagner algorithm is 0.64. For the Bayesian algorithm the: AUROC is 0.87, with a 95% confidence interval of (0.77 0.94). Thus, the Bayesian algorithm performs statistically significantly better than Wagner algorithm.