Comparing Methods for Classifying Clinical Samples Using Proteomics Data
Author Information
Author(s): Sampson Dayle L., Parker Tony J., Upton Zee, Hurst Cameron P.
Primary Institution: Queensland University of Technology
Hypothesis
Can statistical and machine learning approaches effectively classify clinical samples based on proteomics data?
Conclusion
Both Partial Least Squares (PLS) and Support Vector Machines (SVM) are effective for classifying clinical proteomic datasets, with PLS providing additional interpretability.
Supporting Evidence
- PLS and SVM demonstrated strong utility for proteomic classification problems.
- PLS-based classifiers produced models with additional meaningful information.
- SVMs were the most efficient classifier on most datasets tested.
Takeaway
This study looks at different ways to sort medical samples based on proteins. Some methods work better than others, and one method helps us understand the results better.
Methodology
The study compared statistical dimension reduction techniques (PLS and PCA) and machine learning methods (SVM) for classifying clinical proteomic datasets.
Potential Biases
Potential bias due to the selection of datasets and methods used for classification.
Limitations
The study's findings may not generalize across all datasets due to variability in performance among different classification methods.
Digital Object Identifier (DOI)
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