Deep learning decodes species-specific codon usage signatures in Brassica from coding sequences.

ABSTRACT: Plant species discrimination remains a significant challenge in modern genomics, particularly for closely related species with substantial agricultural importance. Current morphological and molecular approaches often lack the resolution needed for reliable differentiation, creating a pressing need for more sophisticated analytical methods. This study demonstrates how deep learning can address this gap by providing high-accuracy classification of four key Brassica species (B. juncea, B. napus, B. oleracea, and B. rapa) using genomic sequence data. We conducted a systematic comparison of seven neural network architectures, focusing on their ability to discriminate between these closely related species. Based on test data, the Multilayer Perceptron achieved 100% classification accuracy with e

SUBMITTER: Shahzad A 

PROVIDER: S-EPMC12480719 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature