"Eukaryotic Genomic Windows for Swarm-Optimized Exon Detection"

"This dataset is derived from the benchmark GENSCAN training set, comprising 380 human genes (238 multi-exon and 142 single-exon sequences). To facilitate deep learning for exon detection, the raw genomic sequences {A, C, G, T} are transformed using one-hot encoding into 4 times numerical matrices. The data is structured using a sliding window approach, where fixed-length windows are labeled based on the annotation of the central nucleotide (Exon vs. Intron).To address the extreme biological class imbalance where non-coding regions vastly outnumber coding exons the Synthetic Minority Over-sampling Technique (SMOTE) is integrated into the pipeline. This technique generates synthetic minority class samples to ensure the model learns robust features for exon identification rather than biasing toward the majority class. The final dataset is organized into a 10-fold cross-validation structure to ensure rigorous evaluation and prevent data leakage."