Cluster-Based Data Balancing for Protein Contact Map Prediction Using MLP Neural Networks

Abstract Class imbalance poses a significant challenge in binary classification tasks, particularly in bioinformatics applications such as protein contact map prediction. This study addresses the tendency of conventional classifiers to misclassify minority class instances by proposing a novel, intelligent data balancing method. The primary objective was to enhance prediction accuracy and sensitivity in protein contact map classification by employing a cluster-based undersampling technique integrated with a multilayer perceptron (MLP) neural network.Protein sequence data were extracted from established bioinformatics databases, and the class imbalance was mitigated using a k-means clustering algorithm. The majority class samples were divided into clusters, and representative instances were selected based on their proximity to cluster boundaries and intra-cluster density. The resulting dataset, balanced at a 4:1 ratio, was used to train and evaluate an MLP classifier optimized with the Levenberg–Marquardt algorithm. Performance metrics such as accuracy, sensitivity, precision, and F1-score were analyzed across multiple experimental groups.The proposed method outperformed conventional random undersampling, achieving an average accuracy of 93% and sensitivity of 61%, compared to 90% and 50% respectively for the baseline approach. These results demonstrate that cluster-aware sampling significantly improves classification performance in imbalanced datasets. This approach offers a practical and effective strategy for enhancing predictive models in bioinformatics. Future research may explore its integration with deep learning architectures and extension to multi-class prediction problems.