Title

Mining Patterns In Disease Classification Forests

Keywords

Biological pathway; Disease phenotype; Pattern mining; Random forests

Abstract

Multiple biological pathways often work together to determine a given disease phenotype. Understanding what these pathways are and how they cooperate in disease-relevant biological processes is critical to our understanding of diseases. Using microarray gene expression data, researchers have developed several methods to rank pathways by their disease relevance. However, the exact set of pathways involved and how they are involved under given disease conditions remain unclear. In this paper, we propose a novel method to first select a robust set of pathways that together best classify a given disease, and then investigate how genes in these pathways interact to determine the phenotype. By applying our method to several disease related microarray gene expression datasets, we detected many disease-relevant interaction patterns supported by evidence from the literature. Our algorithm also achieves higher accuracy in terms of identification of a robust set of disease-relevant pathways when compared with alternative strategies. © 2010.

Publication Date

10-1-2010

Publication Title

Journal of Biomedical Informatics

Volume

43

Issue

5

Number of Pages

820-827

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.jbi.2010.06.004

Socpus ID

77956265190 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/77956265190

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