Mining biologically active patterns in metabolic pathways using microarray expression profiles

Authors:
Hiroshi Mamitsuka;Yasushi Okuno;Atsuko Yamaguchi
Affiliations:
Kyoto University, Uji, Japan;Kyoto University, Kyoto, Japan;Kyoto University, Uji, Japan
Venue:
ACM SIGKDD Explorations Newsletter
Year:
2003

Citing 3
Cited 2

Foundations of statistical natural language processing

Foundations of statistical natural language processing
Analysis of Gene Expression Data with Pathway Scores

Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Model-Based Clustering and Visualization of Navigation Patterns on a Web Site

Data Mining and Knowledge Discovery

A Hierarchical Mixture of Markov Models for Finding Biologically Active Metabolic Paths Using Gene Expression and Protein Classes

CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
A Markov classification model for metabolic pathways

WABI'09 Proceedings of the 9th international conference on Algorithms in bioinformatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.