Mining biologically active patterns in metabolic pathways using microarray expression profiles

We present a new probabilistic framework for analyzing a metabolic pathway with microarray expression profiles. Our purpose is to find biologically significant paths and patterns in a given metabolic pathway. Our approach first builds a Markov model using a graph structure of a known metabolic pathway, and then estimates parameters of a mixture of the Markov models using microarray data, based on an EM algorithm. In our experiments, we used a main pathway of glycolysis to evaluate the effectiveness of our method. We first measured the performance of our method comparing with that of another method, in a supervised learning manner, and found that our method significantly outperformed another method, which was trained by microarray data only. We further analyzed the trained models and obtained a number of new biological findings on frequent patterns (paths) and long-range correlations in a metabolic pathway.

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