Integration of Deep Learning and Metaheuristics for Advanced RNA-Seq Data Analysis: A Rigorous Framework for Biomarker Discovery

Abstract Background The analysis of high-throughput RNA sequencing (RNA-Seq) data is hindered by the challenge of simultaneously optimizing for predictive accuracy, feature parsimony, and biological relevance. Conventional statistical and machine learning methods often fail to address these competing objectives, struggling with the high dimensionality of transcriptomic data and the complex, non-linear interactions between genes. This research bridges deep learning architectures with chaotic metaheuristic optimization to resolve these critical limitations. Results We developed Neuro-MetaRNA, a novel hybrid framework integrating biological attention mechanisms with chaotic multi-objective optimization. Comprehensive benchmarking across TCGA ( 10,340 samples), GTEx ( 17,382 samples), and ENCODE ( 1,200 samples) datasets demonstrated a \(\:17.3\%\) mean improvement in classification accuracy (p; 0.001) and an unprecedented \(\:95.8\%\) feature reduction compared to state-of-the-art methods. The framework achieved a Pareto front with a hypervolume of 0.921, significantly outperforming standard optimizers like NSGA-II (0.782) and MOEA/D (0.801). Crucially, the resulting gene signatures showed \(\:92\%\) overlap with established cancer hallmark pathways. Conclusions Neuro-MetaRNA establishes a new paradigm for RNA-Seq analysis by effectively balancing the trade-offs between accuracy, model complexity, and interpretability. The integration of chaotic dynamics within a Pareto optimization framework proves highly effective for navigating complex biological search spaces. This work provides a powerful, validated tool for biomarker discovery and paves the way for extending these methods to single-cell and spatial transcriptomics.