Gene Ontology (GO) Prediction using Machine Learning Methods
arXiv:1711.00001
Abstract
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.
The results in this paper result from a biased test set, and is therefore not reliable