Employing Enseble Methods in Machine Learning for Improved Prediction of PTM Functionality in Proteins

INTRODUCTION 
Machine learning plays a crucial role in computational biology, enabling the efficient analysis of large datasets for generating reliable predictions [1][2][6]. The Torres Lab's SAPH-ire TFx model has shown promise in predicting the functional significance of PTMs in proteins from different eukaryotic families, potentially aiding in disease identification and treatment [1][2][3]. To enhance the robustness of PTM functionality predictions and gain insights into different prediction approaches, exploring multiple machine learning algorithms through ensemble techniques is important [6][10]. Combining diverse algorithms allows for more reliable outcomes and evaluation of each model's prediction strategies [6][10]. To address the evolving nature of biological data and prevent a decrease in accuracy caused by conceptual drift, continuous model updates are necessary [4][5] . 

As a trainee in the Torres lab, my goal is to improve the current pipeline by integrating ensemble techniques, including the utilization of different models such as One Class SVM alongside SAPHire TFx, and incorporating them with the previous cross-validation pipeline. This comprehensive approach involves cross-comparing the results between the models during the cross-validation process and retraining models if accuracy falls below a specific threshold. By implementing these advancements, I aim to develop a robust framework for analyzing and interpreting PTM functionality, gaining insights into each model's decision patterns, and ensuring more accurate predictions. These efforts will address the challenges posed by evolving PTM datasets and contribute to advancements in the field.