A novel stacking-based classifier for identifying antifreeze protein using latent semantic analysis

Antifreeze proteins (AFPs) are key in combating cold in living organisms and preventing ice morphogenesis. These proteins have applications in cryopreservation, food preservation, and biotechnology. Factors such as accurate prediction of AFP are considered essential for advancing these fields. In this study, a novel method, StackAFP, for predicting antifreeze proteins has been developed using the stacking method and latent semantic analysis (LSA) as the feature extraction technique. Four machine learning algorithms, such as random forest (RF), XGboost (XGB), CatBoost (CAT), and LightGBM (LGBM), were used as the baseline models, and LGBM is employed as the metaclassifier to develop StackAFP. StackAFP is compared with different conventional machine learning to ensure the robustness of the proposed method. StackAFP shows potentiality with an accuracy of 0.9997, a Matthews Correlation Coefficient (MCC), and a Kappa value of 0.9944. StackAFP outperformed the entire applied conventional machine learning model. Finally, it was found that StackAFP also outperformed the existing methods of identifying AFPs. The performance of StackAFP demonstrates its effectiveness, highlights its potential in bioinformatics, and advances our knowledge of AFPs. The main limiting factor is the severe class imbalance in the dataset, which might influence the model generalization. In future studies, we will employ contrastive learning for better feature representation learning and generalization and reliability of the AFP prediction model.