Hybrid Approach for Protein Secondary Structure Prediction with KNN, SVM, and Neural Network Algorithms

Abstract

One of the main challenges in bioinformatics is predicting the structures of macromolecules, particularly nucleic acids and proteins. In this study, we propose a hybrid approach integrating K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Neural Network (NN) algorithms. We perform an in-depth analysis using various metrics, including accuracy, Q3 score, ROC, and precision-recall curves. Based on the RS126 dataset, we compared our hybrid model with individual approaches, revealing that our model achieves an accuracy of 80% and a Q3 score of 86%, outperforming each of the algorithms separately. These results validate the effectiveness of combining models for protein secondary structure prediction (PSSP). We show that the hybrid model outperforms the other models for this task. We also discuss the implications of these results and propose future work to further improve the accuracy and robustness of the model. This approach could have important implications for protein structure modeling, in particular for understanding their three-dimensional structure and function.