Gene Ontology (GO) Prediction using Machine Learning Methods
This work addresses gene function prediction for axon regeneration, but it is incremental as it applies standard methods to a specific biological dataset.
The researchers tackled the problem of predicting whether a gene is involved in axon regeneration using machine learning, achieving a test score of 85.71% with a Random Forest Classifier, which is 4.1% higher than the baseline.
We applied machine learning to predict whether a gene is involved in axon regeneration. We extracted 31 features from different databases and trained five machine learning models. Our optimal model, a Random Forest Classifier with 50 submodels, yielded a test score of 85.71%, which is 4.1% higher than the baseline score. We concluded that our models have some predictive capability. Similar methodology and features could be applied to predict other Gene Ontology (GO) terms.