SciHorizon-GENE: Benchmarking LLM for Life Sciences Inference from Gene Knowledge to Functional Understanding
arXiv:2601.12805v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown growing promise in biomedical research, particularly for knowledge-driven interpretation tasks. However, their ability to reliably reason from gene-level knowledge to functional understanding, a core requirement for knowledge-enhanced cell atlas interpretation, remains largely underexplored. To address this gap, we introduce SciHorizon-GENE, a large-scale gene-centric benchmark constructed from authoritative biological databases. The benchmark integrates curated knowledge for over 190K human genes
The proliferation of large language models (LLMs) requires rigorous benchmarking in specialized domains like life sciences to validate their utility and limitations beyond general applications.
Reliably reasoning from gene-level knowledge to functional understanding is a critical bottleneck in biomedical research, and validated LLM capabilities could dramatically accelerate drug discovery and personalized medicine.
The introduction of a specialized benchmark like SciHorizon-GENE provides a standardized method to evaluate and drive improvements in LLMs' ability to interpret complex biological data.
- · Biomedical AI researchers
- · Pharmaceutical industry
- · Biotech startups
- · Genomic data platforms
- · Traditional bioinformatics methods
- · LLMs with poor biological domain adaptation
LLMs demonstrate improved accuracy in interpreting gene function and disease mechanisms.
Accelerated development of new therapies and diagnostic tools based on AI-driven biological insights.
The integration of advanced AI reasoning becomes a standard component of preclinical and clinical research pipelines, transforming drug development timelines.
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Read at arXiv cs.AI