Title:Computational Model for Prediction of Foxo Protein Employing Ensemble
Learning Algorithm
Volume: 17
Issue: 3
Author(s): Shruti Jain*
Affiliation:
- Department of ECE, Jaypee University of Information Technology, Solan, Himachal Pradesh, India
Keywords:
System biology, degenerative diseases, diabetes, FKHR, random forest, boosted tree.
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.
