Comparative Analysis of Supervised Algorithms for Protein Cluster Classification Using k-mer Image Embeddings - Soares, Giovanna A. P.; Marques, Hannah I. da S.; Dalmolin, Matheus; Barbosa, Raquel de M.; Fernandes, Marcelo A. C. (Anais do Simpósio Brasileiro de Bioinformática (BSB), 2025)
The rapid expansion of protein sequence databases requires effective computational strategies for accurate classification and functional annotation. This work presents a supervised learning framework for protein cluster classification using vector embeddings derived from k-mer image representations. Four supervised algorithms were systematically compared using five-fold stratified cross-validation: Logistic Regression (L2), Random Forest, k-Nearest Neighbors (kNN), and XGBoost. Embeddings extracted from k-mer images served as input features, enabling alignment-free and scalable classification. On the UniRef100 dataset, all models achieved strong performance. Logistic Regression obtained an accuracy of 98.1% and macro F1-score of 0.981, while Random Forest, kNN, and XGBoost achieved even higher accuracies of 99.7%, 99.4%, and 99.8%, respectively. XGBoost presented the best overall results, with an accuracy of 99.85%, F1-score of 0.9985, AUC of 1.000, and the lowest log loss (0.0071). For UniRef90, a more heterogeneous and challenging dataset, a decrease in accuracy and F1 was observed for all methods. Logistic Regression achieved 93.1% accuracy and F1-score of 0.931, while Random Forest, kNN, and XGBoost obtained accuracies of 99.3%, 98.6%, and 99.6%, respectively. Once again, XGBoost showed the best results for UniRef90, with an accuracy of 99.56%, F1-score of 0.9957, AUC of 0.9999, and log loss of 0.0194. The confusion matrices for both datasets indicate that most protein clusters were correctly classified, with only minor misclassifications among the most challenging classes. These findings demonstrate the effectiveness of embedding-based representations and tree-based ensemble methods for robust and interpretable protein cluster classification, even in more complex and diverse datasets.