A Stacking Based Ensemble Learning Approach for Accurate Identification of Tumor Homing Peptides in Precision Cancer Therapeutics

Abstract

The identification of tumor-homing peptides (THPs) plays a pivotal role in the development of targeted cancer therapies and precision medicine. Current THP identification methods still suffer from limited feature representation, moderate predictive performance, and insufficient generalization, highlighting the need for more robust ensemble frameworks. In this study, we propose STHPP, an innovative stacking-based ensemble machine learning approach designed to improve the accuracy and reliability of THP discovery. Two benchmark datasets, referred to as the "main" and "small" datasets of Shoombuatong were collected, merged, and pre-processed in preparation to create a large dataset and then split for training and testing. The STHPP model applies a two-layer ensemble architecture: first layer that aggregates three heterogenous baseline classifiers, Random Forest (RF), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and then second layer applies CatBoost as a meta-classifier for post-processing predictive results of the base models. The two-layer architecture uses model diversity and concepts in ensemble learning to enhance generalization performance. The STHPP framework proposed got outstanding performance with accuracy 0.98, precision 0.97, sensitivity 0.99, specificity 0.97, and a Matthews Correlation Coefficient (MCC) of 0.98. These are better than the performances of current state-of-the-art approaches, which illustrates the effectiveness of using the stacking strategy in complicated peptide classification problems. The finding showcases the potential of STHPP as a strong and scalable computational platform for propelling peptide-based drug discovery research and targeted oncology.