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Development of sparse Bayesian multinomial generalized linear model for multi-class prediction

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Gene expression profiling has been used for many years to classify samples and to gain insights into the molecular mechanisms of phenotypes and diseases... In addition, identification of markers that accurately predict multiple classes of samples, such as those involved in the progression of cancer or other diseases, remains difficult... In this study, we developed a multinomial Probit Bayesian model which utilized the double exponential prior to induce shrinkage and reduce the number of covariates in the model... Genes were ranked based on the p-value of association... Using a cutoff value of 0.05 after Benjamini and Hochberg FDR correction resulted in a final set of 398 genes... Figure 1 shows the posterior mean of parameters associated with each gene... Using the top ten genes obtained from our model, we were able to achieve 86% classification accuracy in the training group and 82% accuracy in the test group... To test the robustness of the model, we switched the training and test groups and evaluated the classification accuracy... We obtained 88% classification accuracy on the new training group and 86% accuracy on the new test group... The classification accuracy by tumor type is shown in Table 1... Taken together, these results suggest that the Bayesian Multinomial Probit model applied to cancer progression data allows for reasonable subclass prediction... Our future plan is to perform resampling on the selection of training and test groups in order to obtain more robust results and to compare the performance of the model to other popular classifiers such as Support Vector Machine and Random Forest.

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Posterior mean of θs associated with gene1 to gene 398.
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Figure 1: Posterior mean of θs associated with gene1 to gene 398.

Mentions: Figure 1 shows the posterior mean of parameters associated with each gene. Using the top ten genes obtained from our model, we were able to achieve 86% classification accuracy in the training group and 82% accuracy in the test group. To test the robustness of the model, we switched the training and test groups and evaluated the classification accuracy. We obtained 88% classification accuracy on the new training group and 86% accuracy on the new test group. The classification accuracy by tumor type is shown in Table 1. Taken together, these results suggest that the Bayesian Multinomial Probit model applied to cancer progression data allows for reasonable subclass prediction.


Development of sparse Bayesian multinomial generalized linear model for multi-class prediction
Posterior mean of θs associated with gene1 to gene 398.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4196030&req=5

Figure 1: Posterior mean of θs associated with gene1 to gene 398.
Mentions: Figure 1 shows the posterior mean of parameters associated with each gene. Using the top ten genes obtained from our model, we were able to achieve 86% classification accuracy in the training group and 82% accuracy in the test group. To test the robustness of the model, we switched the training and test groups and evaluated the classification accuracy. We obtained 88% classification accuracy on the new training group and 86% accuracy on the new test group. The classification accuracy by tumor type is shown in Table 1. Taken together, these results suggest that the Bayesian Multinomial Probit model applied to cancer progression data allows for reasonable subclass prediction.

View Article: PubMed Central - HTML

AUTOMATICALLY GENERATED EXCERPT
Please rate it.

Gene expression profiling has been used for many years to classify samples and to gain insights into the molecular mechanisms of phenotypes and diseases... In addition, identification of markers that accurately predict multiple classes of samples, such as those involved in the progression of cancer or other diseases, remains difficult... In this study, we developed a multinomial Probit Bayesian model which utilized the double exponential prior to induce shrinkage and reduce the number of covariates in the model... Genes were ranked based on the p-value of association... Using a cutoff value of 0.05 after Benjamini and Hochberg FDR correction resulted in a final set of 398 genes... Figure 1 shows the posterior mean of parameters associated with each gene... Using the top ten genes obtained from our model, we were able to achieve 86% classification accuracy in the training group and 82% accuracy in the test group... To test the robustness of the model, we switched the training and test groups and evaluated the classification accuracy... We obtained 88% classification accuracy on the new training group and 86% accuracy on the new test group... The classification accuracy by tumor type is shown in Table 1... Taken together, these results suggest that the Bayesian Multinomial Probit model applied to cancer progression data allows for reasonable subclass prediction... Our future plan is to perform resampling on the selection of training and test groups in order to obtain more robust results and to compare the performance of the model to other popular classifiers such as Support Vector Machine and Random Forest.

No MeSH data available.