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

TL;DR

BIOME-Bench is a new standardized benchmark designed to evaluate large language models' ability to infer biomolecular interactions and elucidate pathway mechanisms from multi-omics data, addressing limitations of current methods.

Contribution

The paper introduces BIOME-Bench, a comprehensive benchmark with evaluation protocols for assessing LLMs in multi-omics pathway analysis, enabling reproducible and systematic evaluation.

Findings

Existing models struggle with fine-grained biomolecular relation types.

Models have difficulty generating accurate pathway-level explanations.

Significant gaps remain in model performance for multi-omics analysis.

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.