Mining of Microarray, Proteomics, and Clinical Data for Improved
Identification of Chronic Fatigue Syndrome

Hongbo Xie, Zoran Obradovic, Slobodan Vucetic

Information Science and Technology Center, Temple University
1805 N. Broad St., Philadelphia, PA 19122



Abstract. 
Chronic Fatigue Syndrome (CFS) is a recently recognized disease
whose pathophysiology is insufficiently understood. The objective of this
study was to explore if identification accuracy of CFS could be improved
using microarray and proteomics data alone or when integrated with clinical
data. 

First, a two-step approach for selection of genetic CFS biomarkers
from microarray data is proposed. The underlying assumption is that CFS is
characterized by deviations in expression of genes from a limited set of
functions. The approach starts by selection of significantly differentially
expressed genes by using standard statistical testing procedure. Using Gene
Ontology (GO) resource, biological functions of the selected genes are
studied to discover the ones that are highly overrepresented by the
selection. Only the selected genes annotated with the most significant
function are selected as biomarkers for identification of CFS. This approach
results in a small set of biomarkers whose function is the most relevant to
CFS. In our experiments Support Vector Machine (SVM) that uses as attributes
genes obtained by the two-step approach achieved higher accuracy than when
using genes obtained by the traditional one-step approach. (e.g. 72% vs 53%
accuracy when selection based on p-value 0.05). Moreover, the finding that
mRNA processing is the most representative function is consistent with the
previously published results. 

In the second part of the study, benefits of
combining microarray and proteomics data in CFS identification were
explored. Using the standard procedure for preprocessing of ProteinChip
data, we developed a proteomics-based predictor of CFS. Our results on the
38 samples with both microarray and ProteinChip data indicates that
predictor combination can provide improved CFS identification (79% accuracy
by a combination when two approaches agree vs. 72% obtained by microarray
alone). However, an important observation is that the achieved accuracy of
CFS identification of  less than 80% is relatively low as compared to some
other diseases, such as cancer. This suggests that identification of CFS
biomarkers is a challenging task that requires significantly larger amounts
of experimental data. 

Finally, we studied the clinical CFS data to discover
factors that explain sources of CFS identification mistakes. We discovered
significant difference in mental health, physical fatigue, and general
fatigue indicators among cases differently classified by microarray and
proteomics methods. This suggests that CSF identification could be improved
by revising definitions of certain clinical conditions.

Full text including tables at:
http://tinyurl.com/qsre2
 i.e.
http://www.camda.duke.edu/camda06/papers/days/thursday/obradovic/paper