BACKGROUND: The analysis of gene expression data shows that many genes display 
similarity in their expression profiles suggesting some co-regulation. Here, we 
investigated the co-expression patterns in gene expression data and proposed a 
correlation-based research method to stratify individuals.
METHODOLOGY/PRINCIPAL FINDINGS: Using blood from rheumatoid arthritis (RA) 
patients, we investigated the gene expression profiles from whole blood using 
Affymetrix microarray technology. Co-expressed genes were analyzed by a 
biclustering method, followed by gene ontology analysis of the relevant 
biclusters. Taking the type I interferon (IFN) pathway as an example, a 
classification algorithm was developed from the 102 RA patients and extended to 
10 systemic lupus erythematosus (SLE) patients and 100 healthy volunteers to 
further characterize individuals. We developed a correlation-based algorithm 
referred to as Classification Algorithm Based on a Biological Signature (CABS), 
an alternative to other approaches focused specifically on the expression 
levels. This algorithm applied to the expression of 35 IFN-related genes showed 
that the IFN signature presented a heterogeneous expression between RA, SLE and 
healthy controls which could reflect the level of global IFN signature 
activation. Moreover, the monitoring of the IFN-related genes during the 
anti-TNF treatment identified changes in type I IFN gene activity induced in RA 
patients.
CONCLUSIONS: In conclusion, we have proposed an original method to analyze genes 
sharing an expression pattern and a biological function showing that the 
activation levels of a biological signature could be characterized by its 
overall state of correlation.