Gene Ontology (GO) Prediction using Machine Learning Methods

TL;DR

This paper demonstrates that machine learning models, particularly a Random Forest classifier, can effectively predict gene involvement in axon regeneration with high accuracy, offering a potential approach for broader GO term predictions.

Contribution

The study introduces a machine learning framework with optimized features and models for predicting gene functions related to axon regeneration, achieving improved accuracy over baselines.

Findings

Random Forest achieved 85.71% test score

Model outperforms baseline by 4.1%

Method can be adapted for other GO term predictions

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.