Combining Probabilistic Graphical Model-based and Knowledge-based Methods for Automatic Reconstruction of Metabolic Pathways (original) (raw)

Background Automatic reconstruction of metabolic pathways for an organism from genomics and transcriptomics data has been a challenging and important problem in bioinformatics. Traditionally, known reference pathways can be mapped into an organism-specific ones based on its genome annotation and protein homology. However, this simple knowledge-based mapping method might produce incomplete pathways and generally cannot predict unknown new relations and reactions. In contrast, ab initio metabolic network construction methods can predict novel reactions and interactions, but its accuracy tends to be low leading to a lot of false positives. Results Here we combine existing pathway knowledge and ab initio Bayesian probabilistic graphical models together in a novel fashion to improve automatic reconstruction of metabolic networks. Specifically, we built a knowledge database containing known gene / protein interactions and metabolic reactions extracted from existing reference pathways. Kno...