Scalable Microbiome Network Inference: Mitigating Sparsity and Computational Bottlenecks in Random Effects Models

The application of Large Language Models (LLMs) and Transformers to biological and healthcare datasets requires the extraction of highly accurate, noise-filtered ecological networks. The Random Effects Model (REM) is a powerful statistical method for inferring microbial interaction networks and identifying keystone species across heterogeneous studies. However, existing implementations in R that rely on single-threaded "Iteratively Reweighted Least Squares" (IRLS) are computationally prohibitive for high-dimensional metagenomic data, creating a significant bottleneck for downstream machine learning pipelines. In this paper, we present Parallel-REM, a highly scalable, Python-based parallel pipeline accelerating large-scale network inference. By integrating robust variance filtering, sparsity checks, and a batched Master-Worker parallelisation strategy using joblib and statsmodels, we resolve native convergence failures associated with sparse biological matrices. Benchmarking on a massive clinical dataset comprising 70,185 samples and 466 optimal species demonstrates a 26.1x speedup over sequential baselines on a 64-core architecture, reducing computation time from days to minutes. Furthermore, statistical validation shows > 99.9% directional concordance with the original R implementation. Parallel-REM democratises large-scale network extraction, providing the high-throughput infrastructure necessary to feed clean, topological and biological features into modern deep learning and Transformer-based diagnostic architectures.