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The chronic fatigue syndrome: A comparative pathway analysis.
Emmert-Streib F.
Journal of Computational Biology 2007; 14:961-972 doi:10.1089/cmb.2007.0041
Summary
Following the 2005 Cold Spring Harbor - Banbury Center CFS Computational Challenge (C3) Workshop, CDC provided data sets from the Wichita in-hospital clinical study to Duke University for use in the Sixth International Conference for the Critical Assessment of Microarray Data Analysis (CAMDA 2006). Duke University founded CAMDA to provide a forum to critically assess different techniques used in microarray data mining. CAMDA’s aim is to establish the state-of-the-art in microarray data mining and to identify progress and highlight the direction for future effort. CAMDA utilizes a community-wide experiment approach, letting the scientific community analyze the same standard data sets. Researchers worldwide are invited to take the CAMDA challenge and those whose results are accepted are invited to present a 25 minute oral presentation. The 2006 CAMDA was the first to use a single common challenge data set, which contained all clinical, gene expression, SNP, and proteomics data from the Wichita clinical study.
To date 10 peer reviewed publications have resulted from the CAMDA challenge. This publication by Dr. Emmert-Streib at the Stowers Institute for Medical Research, Kansas City, Missouri utilized gene expression data from the Wichita in-hospital study to make predictions as to what pathophysiologic pathways might be involved in CFS.Abstract
In this paper, we introduce a method to detect pathological pathways of a disease. We aim to identify biological processes rather than single genes affected by the chronic fatigue syndrome (CFS). So far, CFS has neither diagnostic clinical signals nor abnormalities that could be diagnosed by laboratory examinations. It is also unclear if the CFS represents one disease or can be subdivided in different categories. We use information from clinical trials, the gene ontology (GO) database as well as gene expression data to identify undirected dependency graphs (UDGs) representing biological processes according to the GO database. The structural comparison of UDGs of sick versus non-sick patients allows us to make predictions about the modification of pathways due to pathogenesis.
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