title: A comparative analysis of methods for predicting clinical outcomes using high-dimensional genomic datasets.
abstract: OBJECTIVE
  The objective of this investigation is to evaluate binary prediction methods for predicting disease status using high-dimensional genomic data. The central hypothesis is that the Bayesian network (BN)-based method called efficient Bayesian multivariate classifier (EBMC) will do well at this task because EBMC builds on BN-based methods that have performed well at learning epistatic interactions.
  
  
  METHODS
  We evaluate how well eight methods perform binary prediction using high-dimensional discrete genomic datasets containing epistatic interactions. The methods are as follows: naive Bayes (NB), model averaging NB (MANB), feature selection NB (FSNB), EBMC, logistic regression (LR), support vector machines (SVM), Lasso, and extreme learning machines (ELM). We use a hundred 1000-single nucleotide polymorphism (SNP) simulated datasets, ten 10,000-SNP datasets, six semi-synthetic sets, and two real genome-wide association studies (GWAS) datasets in our evaluation.
  
  
  RESULTS
  In fivefold cross-validation studies, the SVM performed best on the 1000-SNP dataset, while the BN-based methods performed best on the other datasets, with EBMC exhibiting the best overall performance. In-sample testing indicates that LR, SVM, Lasso, ELM, and NB tend to overfit the data.
  
  
  CONCLUSIONS
  EBMC performed better than NB when there are several strong predictors, whereas NB performed better when there are many weak predictors. Furthermore, for all BN-based methods, prediction capability did not degrade as the dimension increased.
  
  
  CONCLUSIONS
  Our results support the hypothesis that EBMC performs well at binary outcome prediction using high-dimensional discrete datasets containing epistatic-like interactions. Future research using more GWAS datasets is needed to further investigate the potential of EBMC.
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
authors.forename: Xia
authors.initials: X
authors.lastname: Jiang
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
authors.forename: Binghuang
authors.initials: B
authors.lastname: Cai
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
authors.forename: Diyang
authors.initials: D
authors.lastname: Xue
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
authors.forename: Xinghua
authors.initials: X
authors.lastname: Lu
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
authors.forename: Gregory F
authors.initials: GF
authors.lastname: Cooper
authors.affiliation: Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
authors.forename: Richard E
authors.initials: RE
authors.lastname: Neapolitan
mesh_terms: D001499:Bayes Theorem; D030541:Databases, Genetic; D004843:Epistasis, Genetic; D055106:Genome-Wide Association Study; D023281:Genomics; D006801:Humans; D016571:Neural Networks, Computer; D011379:Prognosis; D012372:ROC Curve
keywords: Bayesian Classifier; Bayesian Networks; Epistasis; Genomics; High-Dimensional Data; Prediction
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