StackTHPred: Identifying Tumor-Homing Peptides through GBDT-Based Feature Selection with Stacking Ensemble Architecture.One of the major challenges in cancer therapy lies in the limited targeting specificity exhibited by existing anti-cancer drugs.  In this study, we proposed StackTHPred, a novel machine learning-based framework that predicts THPs using optimal features and a stacking architecture.  With an effective feature selection algorithm and three tree-based machine learning algorithms, StackTHPred has demonstrated advanced performance, surpassing existing THP prediction methods.  StackTHPred also offers favorable interpretability, enabling researchers to better understand the intrinsic characteristics of THPs.  Overall, StackTHPred is beneficial for both the exploration and identification of THPs and facilitates the development of innovative cancer therapies.
StackTHPred
StackTHPred: Identifying Tumor-Homing Peptides through GBDT-Based Feature Selection with Stacking Ensemble Architecture.One of the major challenges in cancer therapy lies in the limited targeting specificity exhibited by existing anti-cancer drugs. 
GBDT-Based Feature Selection with Stacking Ensemble Architecture