A Markov Classification Model for Metabolic Pathways

Abstract

The size and complexity of metabolic networks has increased past the point where a researcher is able to intuitively understand all interacting components. Confronted with complexity, biologists must now create models of these networks to identify key relationships of specific interest to their experiments. In this paper focus on the problem of identifying pathways through metabolic networks that relate to a specific biological response. Our proposed model, HME3M, first identifies frequently traversed network paths using a Markov mixture model. Then by employing a hierarchical mixture of experts, separate classifiers are built using information specific to each path and combined into an ensemble classifier the response. We compare the performance of HME3M with logistic regression and support vector machines (SVM) in both simulated and realistic environments. These experiments clearly show HME3M is a highly interpretable model that outperforms common classification methods for large realistic networks and high levels of pathway noise.