Predicting Multigene Transcriptional Outcomes Based on Dual-Stream Mamba Neural Network

The integration of gene perturbation technology with single-cell RNA sequencing and CRISPR editing has promoted the standardization of perturbation effect studies at the single-cell level. How- ever, the diversity of human cell types and computational complexity of perturbation combinations make exhaustive experimental exploration infeasible. In this context, transcriptional prediction models for multi-gene perturbations have undergone a paradigm shift from traditional statistical modeling to deep learning-driven approaches. Nevertheless, challenges persist, including difficulties in adapting to genome-scale applications and limitations to single-gene perturbation analysis. To address these issues, this study proposes a Mamba-based deep learning algorithm that constructs a dual-stream integration framework for multi-gene perturbation response prediction. The model establishes dual-stream architecture to capture both statistical and biological features of gene ex- pression data, circumventing the limitations of single data- or knowledge-driven approaches. The core Mamba component leverages its linear computational complexity and selective memory mechanism to achieve precise modeling of gene-gene interactions while maintaining computational efficiency. Its input-dependent parameterization strategy enables effective mining of complex data features. Analytical results demonstrate that the proposed model exhibits superior performance in gene perturbation prediction while providing biological interpretability, establishing it as an effective methodology.