Computational Model for Prediction of Foxo Protein Employing Ensemble Learning Algorithm

Abstract

Aims: In this paper, Forkhead box O (FOXO) protein using the ensemble learning algorithm is predicted. When FOXO is in excess in the human body, it leads to LNCap prostate cancer cells, and if deficit leading neurodegenerative diseases. Objective: Neurodegenerative diseases, like Alzheimer's and Parkinson's, are neurological illnesses that are caused by damaged brain cells. For prediction of FOXO protein, Gradient Boosted Machine (GBM) and Random forest (RF) techniques are used. Method: The main idea of using GBM is its non-linear nature but it is difficult for any single decision tree to fit all training. To overcome this, an RF algorithm is used. RF combines the results at the end of the process by average or majority rules, while the GBM algorithm combines the results along the way. Results: A total of 29.16% improvement has been observed by RF over GBM. Average square error is also evaluated to check the testing and training of data for 100 trees on 100 tree sizes. Conclusion: In this paper, a computational model for the prediction of FOXO protein using ensemble learning techniques (Random Forest and GBM) has been proposed. If the dataset has many variable features and the prediction accuracy is not as important then RF can be considered. On the other hand, GBMs are better suited for datasets that have very few or fewer input features and where high accuracy predictions are required. However, there are instances when either GBM or RF can perform equally well depending on how they are tuned.