Protein Structure Prediction Using CNN-Based Feature Extraction

Predicting protein structures is essential for understanding biological functions and advancing drug discovery. In this project, we developed a stacking-based model that integrates various machine learning techniques, including models like Support Vector Machines (SVM), Neural Networks (ANN), and Random Forests (RF) Feature extraction techniques were employed to identify key patterns in protein sequence data, enhancing the quality of inputs for the predictive models. By stacking these algorithms, we combined their strengths, resulting in improved prediction accuracy for protein structures. Our results show that the stacking-based approach significantly outperforms individual models, offering a more robust solution for challenging protein sequences.