BIOME-Bench: A Benchmark for Biomolecular Interaction Inference and Multi-Omics Pathway Mechanism Elucidation from Scientific Literature

• URL: http://arxiv.org/abs/2512.24733v1
• Date: Wed, 31 Dec 2025 09:01:27 GMT
• Title: BIOME-Bench: A Benchmark for Biomolecular Interaction Inference and Multi-Omics Pathway Mechanism Elucidation from Scientific Literature
• Authors: Sibo Wei, Peng Chen, Lifeng Dong, Yin Luo, Lei Wang, Peng Zhang, Wenpeng Lu, Jianbin Guo, Hongjun Yang, Dajun Zeng,
• Abstract summary: BIOME-Bench is constructed via a rigorous four-stage workflow to evaluate two core capabilities of large language models (LLMs) in multi-omics analysis.<n>We develop evaluation protocols for both tasks and conduct comprehensive experiments across multiple strong contemporary models.
• Score: 12.185152549393152
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-omics studies often rely on pathway enrichment to interpret heterogeneous molecular changes, but pathway enrichment (PE)-based workflows inherit structural limitations of pathway resources, including curation lag, functional redundancy, and limited sensitivity to molecular states and interventions. Although recent work has explored using large language models (LLMs) to improve PE-based interpretation, the lack of a standardized benchmark for end-to-end multi-omics pathway mechanism elucidation has largely confined evaluation to small, manually curated datasets or ad hoc case studies, hindering reproducible progress. To address this issue, we introduce BIOME-Bench, constructed via a rigorous four-stage workflow, to evaluate two core capabilities of LLMs in multi-omics analysis: Biomolecular Interaction Inference and end-to-end Multi-Omics Pathway Mechanism Elucidation. We develop evaluation protocols for both tasks and conduct comprehensive experiments across multiple strong contemporary models. Experimental results demonstrate that existing models still exhibit substantial deficiencies in multi-omics analysis, struggling to reliably distinguish fine-grained biomolecular relation types and to generate faithful, robust pathway-level mechanistic explanations.

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