Abstract:Predicting microbial operon co-membership requires integrating two complementary biological signals: protein-scale molecular identity and genome-context organization. While recent biological foundation models provide powerful representations of each view independently, naive concatenation of these modalities ignores a key biological property -- protein identity and genomic context may agree when adjacent genes form a coherent functional module, or conflict when sequence similarity is misleading but genomic layout indicates independent regulation. We present MicroFuse, a protein-to-genome expert fusion framework that integrates structure-aware protein representations from ProstT5 with genome-context representations from Bacformer through a four-expert Mixture-of-Experts module (protein, genome-context, agreement, and conflict experts) with a learned soft router. Training combines binary cross-entropy with symmetric cross-modal InfoNCE alignment and disagreement-weighted supervised contrastive shaping. We further construct OG-Operon100K, a 100,000-pair scaffold-level benchmark from the OMG metagenomic corpus with biologically grounded positive and negative criteria. On OG-Operon100K, MicroFuse achieves the strongest AUROC, AUPRC, mAP, and mAR among ProstT5-only, Bacformer-only, and Concat MLP baselines. Ablations identify cross-modal contrastive alignment as the dominant component, and a hard sequence-conflict subset reveals MicroFuse's largest gains precisely in biologically ambiguous cases where protein identity alone is misleading.
| Subjects: | Machine Learning (cs.LG); Biomolecules (q-bio.BM); Genomics (q-bio.GN); Quantitative Methods (q-bio.QM) |
| Cite as: | arXiv:2605.08815 [cs.LG] |
| (or arXiv:2605.08815v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.08815 arXiv-issued DOI via DataCite (pending registration) |
From: Seungik Cho [view email]
[v1]
Sat, 9 May 2026 09:07:11 UTC (718 KB)
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