aceGenolator enables protein function interpretation using a multimodal large language model fusing genomic and structural interpretation with natural language interaction
Background Decoding the genetic code to unveil its genome functionality is a monumental task which would greatly advance the understanding of disease mechanisms and development of targeted treatments. Although large language models (LLMs) have transformed natural language processing across diverse domains, translating the complex language of DNA into human-readable form remains challenging due to genomic data complexity and unexplored regions of the human genome. Current language models either are capable of pro…
On 34,743 held-out questions about unseen proteins, Genolator scored an MCC of 0.959 on confirm-or-deny function questions, versus 0.913 for a specialist XGBoost classifier and 0.179 for GPT 4.1.
Open-ended answers were judged by a GPT 4.1-based automatic mapping rather than experts, exact matches were rare, and the model was not hyperparameter-tuned or tested across multiple species.
Evidence
- Peer-reviewedGenome Biology2026-09-16
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Truvace Impact Record TRV-2026-1133, v3: “Genolator enables protein function interpretation using a multimodal large language model fusing genomic and structural interpretation with natural language interaction.” Truvace, 2026-11-04. /record/TRV-2026-1133 (accessed at citation time). sha256 9b5991be8968c67e…
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