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A comparison of machine learning and Bayesian modelling for molecular serotyping.
Richard Newton1, Lorenz Wernisch2
1MRC Biostatistics Unit, Robinson Way, Cambridge, CB2 0SR, UK. richard.newton@mrc-bsu.cam.ac.uk.
BMC Genomics
|August 13, 2017
Summary
Machine learning algorithms, including Gradient Boosting Machines and Random Forests, now outperform the original Bayesian model for Streptococcus pneumoniae serotype classification. A combined approach using the Bayesian Model and Gradient Boosting Machine is best for serotypes with limited training data.
Area of Science:
- Microbiology and Infectious Diseases
- Computational Biology and Bioinformatics
- Genomics and Molecular Biology
Background:
- Streptococcus pneumoniae is a leading cause of infant mortality globally.
- Accurate pneumococcal serotyping is crucial for monitoring vaccine efficacy.
- Genomic microarrays offer an effective method for molecular serotyping.
Purpose of the Study:
- To investigate the application of machine learning methods for pneumococcal serotype classification.
- To compare the performance of machine learning algorithms against a previously developed empirical Bayesian model.
- To explore a generic strategy for complementing or replacing probabilistic models with machine learning classifiers as data availability increases.
Main Methods:
- Comparison of an empirical Bayesian model with Gradient Boosting Machines and Random Forests.
- Development of an iterative analysis to create artificial training data for serotype mixtures.
- Generation of synthetic training data by combining raw data from single-serotype arrays to address data scarcity.
Main Results:
- Machine learning algorithms, with an enhanced training set, demonstrated superior performance compared to the original Bayesian model.
- For serotypes with insufficient training data, a hybrid approach combining the Bayesian Model and Gradient Boosting Machine yielded the best results.
- The study highlights machine learning's effectiveness in biological data classification and uncovering subtle biological insights.
Conclusions:
- Machine learning methods, particularly when trained on extensive and augmented datasets, offer improved accuracy in pneumococcal serotype classification.
- A combined Bayesian and machine learning approach provides a robust strategy for classifying serotypes with limited available data.
- This research demonstrates the utility of machine learning beyond classification, extending to the discovery of novel biological insights.
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