Bag of Nave Bayes: biomarker selection and classification from - PowerPoint PPT Presentation

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Bag of Nave Bayes: biomarker selection and classification from Genome-Wide SNP data Francesco Sambo Context Complex disease, with hypothesized but still not understood genetic origin Genome Wide Association Study (GWAS) O(10 6 ) Single

Bag of Naïve Bayes: biomarker selection and classification from Genome-Wide SNP data Francesco Sambo
Context Complex disease, with hypothesized but still not understood genetic origin Genome Wide Association Study (GWAS) • O(10 6 ) Single Nucleotyde Polymorphisms (SNPs) • O(10 3 ) case / control individuals Objectives: 1.Biomarker Selection 2.Classification
Bag of Naïve Bayes (BoNB) • Both classification and biomarker selection • Based on Naïve Bayes classification • Main features: a) Ensamble of Naïve Bayes Classifiers (NBC), robustness b) Novel strategy for ranking and selecting attributes for each NBC, attribute independence c) Permutation-based procedure for biomarker selection, based on marginal utility.
Bagging (Bootstrap AGGregatING) Bootstrap Ensemble of NBCs GWAS Data D 1 NBC 1 Prediction 1 SNPs subjects D Weighted Prediction D B NBC B Prediction B • B bootstrap replicates, sampled with replacement from D • B Naive Bayes Classifiers, each trained on a D b • Outcome: average of the B predictions
NBC attribute selection (SNPs) Bootstrap Ensemble of NBCs GWAS Data Attribute Selection D 1 NBC 1 Prediction 1 SNPs oob 1 subjects D Weighted Prediction oob B D B NBC B Prediction B • Ranking: training error when SNP is used as single attribute • Selection: top ranked, uncorrelated SNPs ( r 2 < 0.1 if dist < 1 Mb ) • Number of selected attributes increased, as long as classification accuracy increases on the Out-Of-Bag (OOB) sets
Biomarker Selection Bootstrap Ensemble of NBCs GWAS Data Attribute Selection D 1 NBC 1 Prediction 1 SNPs oob 1 subjects D Biomarker Selection Weighted Prediction oob B D B NBC B Prediction B • Random permutation of the genotype of NBC attributes in OOBs • Measure decrease in accuracy on OOBs • Wilcoxon signed-rank test for significance
Results WTCCC case / control study on Type 1 Diabetes • 458376 SNPs, 1963 T1D cases, 2938 controls Biomarker Selection Predictive accuracy Matthews Correlation Coefficient rs ID chr gene rs6679677 1 RSBN1 rs9273363 6 MHC region rs3101942 6 MHC region rs492899 6 MHC region rs6936863 6 MHC region rs805301 6 MHC region rs9275418 6 MHC region rs2856688 6 MHC region
Conclusions • BoNB effective for both classification and biomarker selection • Advantages of bagging:  Higher generalization ability  Sound and principled procedure for biomarker selection • Advantages of Naïve Bayes:  No pre-specified model of genetic effect  Seamless handling of missing values

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