Rule-Based Protein Classification through Multi-Phase Feature Extraction Technique

Protein sequence classification is a fundamental step toward functional annotation and biological analysis; however, most of the existing approaches rely on computationally expensive models or flat feature integration with limited interpretability. This paper proposes a multi-phase rule-based protein classification framework that hierarchically integrates biologically meaningful features. An early-exit decision strategy is employed. Feature extraction is performed across four progressive phases comprising chemical properties, hydropathy characteristics, behavioral sequence patterns through n-gram and six-letter exchange encoding, and functional attributes via normalized distance-based encoding. Family-specific knowledge matrices constructed exclusively from training data enable deterministic min-max rule evaluation at each phase for efficient candidate pruning and early termination. Experiments conducted on 8,555 human protein sequences spanning ten families achieve an average classification accuracy of 97.66%. 5-fold, 25-fold, and 45-fold cross-validation statistically confirm performance stability under class-imbalanced conditions. Further cross-species validation on zebrafish and mouse protein datasets yields accuracies of 96.11% and 94.89%, respectively, demonstrating robust generalization across diverse biological distributions. Comparative evaluation against machine-learning-based and transformer-based baselines shows that the proposed framework achieves a superior accuracy-efficiency trade-off while maintaining an overall time complexity of O(nlog n).