A Dual-Stream Residual Framework for Microbiome-Driven Precision Nutrition and Dietary Optimization

Abstract

Contemporary nutritional science frequently relies on generalized dietary heuristics, fundamentally overlooking the gut mi-crobiome’s critical role in individual metabolic variability. Standard dietary pipelines utilize static metrics such as age and weight, ignoring the complex, high-dimensional microbial compositions that drive personalized nutrient absorption. While commercial metagenomic sequencing provides high-fidelity biological data, translating sparse, high-dimensional DNA k-mer signatures into safe, deterministic dietary interventions remains a computational bottleneck. This paper proposes the Bio-Perceiver, a Dual-Stream Tabular ResNet framework engineered to synthesize genomic arrays with clinical metadata. Utilizing a distributed pipeline, we harvested raw FASTQ signatures and executed strict dimensionality reduction, compressing over 12,000 sparse 16S rRNA markers into a dense 9-dimensional input tensor to prevent neural network overfitting. To bypass gradient corruption, we introduce a customized TabularResBlock architecture coupled with a Modality Dropout mechanism that forces robust multi-omic learning. The network predicts biological affinity across a curated 796-item clinical food catalog. During deployment, a deterministic 0/1 Knapsack algorithmic layer enforces hard caloric boundaries against Total Daily Energy Expenditure (TDEE) and strict dietary identity constraints (e.g., vegetarianism). Evaluated against rigorous baseline metrics, the framework achieved an NDCG@10 of 0.942. Furthermore, Integrated Gradients are utilized within the presentation layer to provide axiomatic Explainable AI (XAI) visualizations, establishing a transparent, data-driven foundation for precision dietetics.