title: Combining gene expression, demographic and clinical data in modeling disease: a case study of bipolar disorder and schizophrenia.
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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A, 3001 Leuven, Belgium. jan.struyf@cs.kuleuven.be
authors.forename: Jan
authors.initials: J
authors.lastname: Struyf
authors.forename: Seth
authors.initials: S
authors.lastname: Dobrin
authors.forename: David
authors.initials: D
authors.lastname: Page
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001714:Bipolar Disorder / Q000235:genetics*; D019295:Computational Biology; D003663:Decision Trees; D005260:Female; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D015233:Models, Statistical; D012372:ROC Curve; D012189:Retrospective Studies; D012559:Schizophrenia / Q000235:genetics*