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Identification of Disease Genes Using Gene Expression and Protein–Protein Interaction Data

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Scalable Pattern Recognition Algorithms

Abstract

One of the most important and challenging problems in functional genomics is how to select the disease genes. In this chapter, a computational method is reported to identify disease genes, judiciously integrating the information of gene expression profiles and shortest path analysis of protein-protein interaction networks. While the gene expression profiles have been used to select differentially expressed genes as disease genes using mutual information-based maximum relevance-maximum significance framework, the functional protein association network has been used to study the mechanism of diseases. Extensive experimental study on colorectal cancer establishes the fact that the genes identified by the integrated method have more colorectal cancer genes than the genes identified from the gene expression profiles alone. All these results indicate that the integrated method is quite promising and may become a useful tool for identifying disease genes.

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  1. Indian Statistical Institute, Kolkata, West Bengal, India

    Pradipta Maji & Sushmita Paul

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  1. Pradipta Maji
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  2. Sushmita Paul
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Correspondence to Pradipta Maji .

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Maji, P., Paul, S. (2014). Identification of Disease Genes Using Gene Expression and Protein–Protein Interaction Data. In: Scalable Pattern Recognition Algorithms. Springer, Cham. https://doi.org/10.1007/978-3-319-05630-2_6

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  • DOI: https://doi.org/10.1007/978-3-319-05630-2_6

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-05630-2

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