Improving Protein Function Prediction with Reconciliation Methods
Author Information
Author(s): Guillaume Obozinski, Gert Lanckriet, Charles Grant, Michael I. Jordan, William Stafford Noble
Hypothesis
Can reconciliation methods improve the consistency and accuracy of protein function predictions derived from independent classifiers?
Conclusion
Isotonic regression is the most effective reconciliation method for protein function prediction, consistently yielding high precision across various evaluation modes.
Supporting Evidence
- Isotonic regression generally outperformed other reconciliation methods across various evaluation modes.
- Many reconciliation methods resulted in lower precision than the original predictions.
- The study identified that isotonic regression can leverage the structure of the Gene Ontology to improve classification.
Takeaway
This study shows that when predicting what proteins do, using a method that makes sure predictions make sense together can help scientists get better answers.
Methodology
The study employed support vector machines (SVMs) for initial predictions and applied various reconciliation methods to improve consistency with Gene Ontology.
Potential Biases
Some reconciliation methods may yield lower precision than unreconciled estimates, potentially misleading interpretations.
Limitations
The performance of reconciliation methods can vary significantly based on the specific terms and contexts, particularly for smaller terms.
Statistical Information
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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