Dynamic Reachability Distance-Based Dense Clustering Method for Protein-DNA Sites Noise Reduction Based on the Predicted Binding Site Probabilities

With the development of bioinformatics and machine learning techniques, protein-DNA binding site prediction has become a key topic in understanding gene regulation and molecular recognition. However, methods relying solely on sequence information often ignore important spatial distribution features in protein structures, leading to inadequate identification of complex binding sites. This paper introduces a novel protein-DNA binding site prediction method, FRD-Bind, which utilizes the Random Forest and the DRD dense clustering algorithms. By combining sequence and structural features, FRD- Bind improves the prediction accuracy. The Random Forest classifier is used to determine the predicted scores of residues, and the DRD dense clustering algorithm is then applied to reduce noise and enhance the accuracy of binding site prediction. Experimental results show that FRD-Bind achieves an accuracy rate(ACC) of 94%, which is superior to other sequence- and structure-based prediction methods. This method is particularly effective for tasks involving unbalanced positive and negative samples and helps to accurately predict protein-DNA binding sites.