RareCollab: an LLM-powered framework for multimodal reasoning in Mendelian disease diagnosis
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| author | Qi, Guantong Wang, Jiasheng Chong, Mei Ling Shaik, Zahid Li, Shenglan Yamamoto, Shinya Ruzhnikov, Maura R. Z. Bonner, Devon E. Carter, Jennefer N. Smith, Kevin S. Wheeler, Matthew T. Montgomery, Stephen B. Bernstein, Jonathan A. Pasupuleti, Sasidhar Network, Undiagnosed Diseases Liu, Pengfei Chen, Hu Liu, Zhandong |
| author_facet | Qi, Guantong Wang, Jiasheng Chong, Mei Ling Shaik, Zahid Li, Shenglan Yamamoto, Shinya Ruzhnikov, Maura R. Z. Bonner, Devon E. Carter, Jennefer N. Smith, Kevin S. Wheeler, Matthew T. Montgomery, Stephen B. Bernstein, Jonathan A. Pasupuleti, Sasidhar Network, Undiagnosed Diseases Liu, Pengfei Chen, Hu Liu, Zhandong |
| contents | Rare disease diagnosis increasingly relies on integrating genomic, phenotypic and transcriptomic evidence, yet these signals remain difficult to reconcile within a common interpretive framework. Here we present RareCollab, an LLM-powered framework for multimodal reasoning in Mendelian disease diagnosis that integrates more than 100 diagnostic evidence signals across DNA, RNA, phenotype, curated variant-level knowledge, and in-silico pathogenicity evidence. This design enables large language models to operate as calibrated, interpretable reasoning modules rather than as a single end-to-end ranker. We applied RareCollab to 890 patients from three cohorts, including 119 Undiagnosed Diseases Network probands with paired DNA and RNA data, constituting a large systematic benchmark for multimodal rare disease diagnosis under paired genomic and transcriptomic evaluation. In this real-world multimodal benchmark, RareCollab prioritized 94% of diagnostic genes within the top 10. Across recall thresholds from top 1 to top 10, it consistently outperformed proprietary phenotype-driven LLM baselines including Claude Sonnet 4.6 and GPT-5-mini by more than 25% on average and surpassed established state-of-the-art variant prioritization methods by 11%-24%. RareCollab also reshapes the diagnostic contribution of RNA evidence, which contributes to prioritization of the diagnostic gene in 35% of cases (42/119). Together, these results establish RareCollab as a scalable and interpretable framework for multimodal rare disease diagnosis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_04058 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RareCollab: an LLM-powered framework for multimodal reasoning in Mendelian disease diagnosis Qi, Guantong Wang, Jiasheng Chong, Mei Ling Shaik, Zahid Li, Shenglan Yamamoto, Shinya Ruzhnikov, Maura R. Z. Bonner, Devon E. Carter, Jennefer N. Smith, Kevin S. Wheeler, Matthew T. Montgomery, Stephen B. Bernstein, Jonathan A. Pasupuleti, Sasidhar Network, Undiagnosed Diseases Liu, Pengfei Chen, Hu Liu, Zhandong Genomics Rare disease diagnosis increasingly relies on integrating genomic, phenotypic and transcriptomic evidence, yet these signals remain difficult to reconcile within a common interpretive framework. Here we present RareCollab, an LLM-powered framework for multimodal reasoning in Mendelian disease diagnosis that integrates more than 100 diagnostic evidence signals across DNA, RNA, phenotype, curated variant-level knowledge, and in-silico pathogenicity evidence. This design enables large language models to operate as calibrated, interpretable reasoning modules rather than as a single end-to-end ranker. We applied RareCollab to 890 patients from three cohorts, including 119 Undiagnosed Diseases Network probands with paired DNA and RNA data, constituting a large systematic benchmark for multimodal rare disease diagnosis under paired genomic and transcriptomic evaluation. In this real-world multimodal benchmark, RareCollab prioritized 94% of diagnostic genes within the top 10. Across recall thresholds from top 1 to top 10, it consistently outperformed proprietary phenotype-driven LLM baselines including Claude Sonnet 4.6 and GPT-5-mini by more than 25% on average and surpassed established state-of-the-art variant prioritization methods by 11%-24%. RareCollab also reshapes the diagnostic contribution of RNA evidence, which contributes to prioritization of the diagnostic gene in 35% of cases (42/119). Together, these results establish RareCollab as a scalable and interpretable framework for multimodal rare disease diagnosis. |
| title | RareCollab: an LLM-powered framework for multimodal reasoning in Mendelian disease diagnosis |
| topic | Genomics |
| url | https://arxiv.org/abs/2602.04058 |