Comparative study analysis.

DNA splice junction classification is a crucial job in computational biology. The challenge is to predict the junction type (IE, EI, or N) from a given DNA sequence. Predicting junction type is crucial for understanding gene expression patterns, disease causes, splicing regulation, and gene structure. The location of the regions where exons are joined, and introns are removed during RNA splicing is very difficult to determine because no universal rule guides this process. This study presents a two-layer hybrid approach inspired by ensemble learning to overcome this challenge. The first layer applies the grey wolf optimizer (GWO) for feature selection. GWO’s exploration ability allows it to efficiently search a vast feature space, while its exploitation ability refines promising areas, thus leading to a more reliable feature selection. The selected features are then fed into the second layer, which employs a classification model trained on the retrieved features. Using cross-validation, the proposed method divides the DNA splice junction dataset into training and test sets, allowing for a thorough examination of the classifier’s generalization ability. The ensemble model is trained on various partitions of the training set and tested on the remaining held-out fold. This process is performed for each fold, comprehensively evaluating the classifier’s performance. We tested our method using the StatLog DNA dataset. Compared to various machine learning models for DNA splice junction prediction, the proposed GWO+SVM ensemble method achieved an accuracy of 96%. This finding suggests that the proposed ensemble hybrid approach is promising for DNA splice junction classification. The implementation code for the proposed approach is available at https://github.com/EFHamouda/DNA-splice-junction-prediction.