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Results: We introduce B-MASTER, a scalable Bayesian multivariate regression framework combining L1 sparsity and L2 group shrinkage to identify essential cross-metabolite regulators. A Gibbs sampler enables near-linear computational scaling, supporting models with millions of parameters. The method is supported by theoretical guarantees, including posterior contraction and selection consistency. Analysis of colorectal cancer microbiome-metabolome data reveals key microbial genera that govern global and cancer-associated metabolite patterns, highlighting system-level regulatory structure.
Availability: The B-MASTER code, including demonstration scripts, is available at this https URL. An archived snapshot of the code corresponding to this manuscript is available on Zenodo with DOI: https://doi.org/10.5281/zenodo.20484958.
From: Priyam Das [view email]
[v1]
Sun, 8 Dec 2024 17:13:06 UTC (9,885 KB)
[v2]
Tue, 27 May 2025 20:32:05 UTC (21,707 KB)
[v3]
Fri, 12 Sep 2025 17:57:31 UTC (10,527 KB)
[v4]
Mon, 1 Jun 2026 07:48:56 UTC (16,804 KB)
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