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This article is part of the supplement: Symposium of Computations in Bioinformatics and Bioscience (SCBB07)

Open Access Research

MCM-test: a fuzzy-set-theory-based approach to differential analysis of gene pathways

Lily R Liang1*, Vinay Mandal2, Yi Lu3 and Deepak Kumar4*

Author Affiliations

1 Department of Computer Science and Information Technology, University of the District of Columbia, Washington, D.C., USA

2 Department of Computer Science, Wayne State University, Michigan, USA

3 Department of Computer Science, Prairie View A&M University, Texas, USA

4 Department of Biological and Environmental Sciences, University of the District of Columbia, Washington, D.C., USA

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BMC Bioinformatics 2008, 9(Suppl 6):S16  doi:10.1186/1471-2105-9-S6-S16

Published: 28 May 2008

Abstract

Background

Gene pathway can be defined as a group of genes that interact with each other to perform some biological processes. Along with the efforts to identify the individual genes that play vital roles in a particular disease, there is a growing interest in identifying the roles of gene pathways in such diseases.

Results

This paper proposes an innovative fuzzy-set-theory-based approach, Multi-dimensional Cluster Misclassification test (MCM-test), to measure the significance of gene pathways in a particular disease. Experiments have been conducted on both synthetic data and real world data. Results on published diabetes gene expression dataset and a list of predefined pathways from KEGG identified OXPHOS pathway involved in oxidative phosphorylation in mitochondria and other mitochondrial related pathways to be deregulated in diabetes patients. Our results support the previously supported notion that mitochondrial dysfunction is an important event in insulin resistance and type-2 diabetes.

Conclusion

Our experiments results suggest that MCM-test can be successfully used in pathway level differential analysis of gene expression datasets. This approach also provides a new solution to the general problem of measuring the difference between two groups of data, which is one of the most essential problems in most areas of research.