J Am Med Inform Assoc - A comparative analysis of methods for predicting clinical outcomes using high-dimensional genomic datasets.

Tópicos

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Resumo

JECTIVE: 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.METHOD: 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.DISCUSSION: 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.

Resumo Limpo

jectiv object investig evalu binari predict method predict diseas status use highdimension genom data central hypothesi bayesian network bnbase method call effici bayesian multivari classifi ebmc will well task ebmc build bnbase method perform well learn epistat interactionsmethod evalu well eight method perform binari predict use highdimension discret genom dataset contain epistat interact method follow naiv bay nb model averag nb manb featur select nb fsnb ebmc logist regress lr support vector machin svm lasso extrem learn machin elm use hundr singl nucleotid polymorph snp simul dataset ten snp dataset six semisynthet set two real genomewid associ studi gwas dataset evaluationresult fivefold crossvalid studi svm perform best snp dataset bnbase method perform best dataset ebmc exhibit best overal perform insampl test indic lr svm lasso elm nb tend overfit datadiscuss ebmc perform better nb sever strong predictor wherea nb perform better mani weak predictor furthermor bnbase method predict capabl degrad dimens increasedconclus result support hypothesi ebmc perform well binari outcom predict use highdimension discret dataset contain epistaticlik interact futur research use gwas dataset need investig potenti ebmc

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