Machine learning methods for metabolic pathway prediction

Background: A key challenge in systems biology is the reconstruction of an organism's metabolic network from its genome sequence. One strategy for addressing this problem is to predict which metabolic pathways, from a reference database of known pathways, are present in the organism, based on the annotated genome of the organism. Results: To quantitatively validate methods for pathway prediction, we developed a large "gold standard" dataset of 5,610 pathway instances known to be present or absent in curated metabolic pathway databases for six organisms. We defined a collection of 123 pathway features, whose information content we evaluated with respect to the gold standard. Feature data were used as input to an extensive collection of machine learning (ML) methods, including na

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Joseph M. Dale, Liviu Popescu, Peter D. Karp
• Keywords: BMCBI 2010, Gold Standard, Metabolic Pathway Databases, Pathway Prediction