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Open Access Highly Accessed Research article

Combining gene expression, demographic and clinical data in modeling disease: a case study of bipolar disorder and schizophrenia

Jan Struyf1*, Seth Dobrin23 and David Page4

Author Affiliations

1 Department of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A, 3001 Leuven, Belgium

2 Marshfield Clinic Research Foundation, Marshfield, WI 54449, USA

3 Monsanto Company, High-Throughput Genetics, 3302 SE, Convenience Boulevard, Ankeny, IA 50021, USA

4 Department of Biostatistics and Medical Informatics and Department of Computer Sciences, University of Wisconsin, Medical Science Center, 1300 University Avenue, Madison, WI 53706, USA

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BMC Genomics 2008, 9:531  doi:10.1186/1471-2164-9-531

Published: 7 November 2008

Abstract

Background

This paper presents a retrospective statistical study on the newly-released data set by the Stanley Neuropathology Consortium on gene expression in bipolar disorder and schizophrenia. This data set contains gene expression data as well as limited demographic and clinical data for each subject. Previous studies using statistical classification or machine learning algorithms have focused on gene expression data only. The present paper investigates if such techniques can benefit from including demographic and clinical data.

Results

We compare six classification algorithms: support vector machines (SVMs), nearest shrunken centroids, decision trees, ensemble of voters, naïve Bayes, and nearest neighbor. SVMs outperform the other algorithms. Using expression data only, they yield an area under the ROC curve of 0.92 for bipolar disorder versus control, and 0.91 for schizophrenia versus control. By including demographic and clinical data, classification performance improves to 0.97 and 0.94 respectively.

Conclusion

This paper demonstrates that SVMs can distinguish bipolar disorder and schizophrenia from normal control at a very high rate. Moreover, it shows that classification performance improves by including demographic and clinical data. We also found that some variables in this data set, such as alcohol and drug use, are strongly associated to the diseases. These variables may affect gene expression and make it more difficult to identify genes that are directly associated to the diseases. Stratification can correct for such variables, but we show that this reduces the power of the statistical methods.