Integrating Large Language Models for Genetic Variant Classification
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909380334583808 |
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| author | Boulaimen, Youssef Fossi, Gabriele Outemzabet, Leila Jeanray, Nathalie Levenets, Oleksandr Gerart, Stephane Vachenc, Sebastien Raieli, Salvatore Giemza, Joanna |
| author_facet | Boulaimen, Youssef Fossi, Gabriele Outemzabet, Leila Jeanray, Nathalie Levenets, Oleksandr Gerart, Stephane Vachenc, Sebastien Raieli, Salvatore Giemza, Joanna |
| contents | The classification of genetic variants, particularly Variants of Uncertain Significance (VUS), poses a significant challenge in clinical genetics and precision medicine. Large Language Models (LLMs) have emerged as transformative tools in this realm. These models can uncover intricate patterns and predictive insights that traditional methods might miss, thus enhancing the predictive accuracy of genetic variant pathogenicity.
This study investigates the integration of state-of-the-art LLMs, including GPN-MSA, ESM1b, and AlphaMissense, which leverage DNA and protein sequence data alongside structural insights to form a comprehensive analytical framework for variant classification. Our approach evaluates these integrated models using the well-annotated ProteinGym and ClinVar datasets, setting new benchmarks in classification performance. The models were rigorously tested on a set of challenging variants, demonstrating substantial improvements over existing state-of-the-art tools, especially in handling ambiguous and clinically uncertain variants.
The results of this research underline the efficacy of combining multiple modeling approaches to significantly refine the accuracy and reliability of genetic variant classification systems. These findings support the deployment of these advanced computational models in clinical environments, where they can significantly enhance the diagnostic processes for genetic disorders, ultimately pushing the boundaries of personalized medicine by offering more detailed and actionable genetic insights. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_05055 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Integrating Large Language Models for Genetic Variant Classification Boulaimen, Youssef Fossi, Gabriele Outemzabet, Leila Jeanray, Nathalie Levenets, Oleksandr Gerart, Stephane Vachenc, Sebastien Raieli, Salvatore Giemza, Joanna Genomics Artificial Intelligence Machine Learning 68T07 (Primary) 92D20, 92C40 (Secondary) I.2.7; J.3 The classification of genetic variants, particularly Variants of Uncertain Significance (VUS), poses a significant challenge in clinical genetics and precision medicine. Large Language Models (LLMs) have emerged as transformative tools in this realm. These models can uncover intricate patterns and predictive insights that traditional methods might miss, thus enhancing the predictive accuracy of genetic variant pathogenicity. This study investigates the integration of state-of-the-art LLMs, including GPN-MSA, ESM1b, and AlphaMissense, which leverage DNA and protein sequence data alongside structural insights to form a comprehensive analytical framework for variant classification. Our approach evaluates these integrated models using the well-annotated ProteinGym and ClinVar datasets, setting new benchmarks in classification performance. The models were rigorously tested on a set of challenging variants, demonstrating substantial improvements over existing state-of-the-art tools, especially in handling ambiguous and clinically uncertain variants. The results of this research underline the efficacy of combining multiple modeling approaches to significantly refine the accuracy and reliability of genetic variant classification systems. These findings support the deployment of these advanced computational models in clinical environments, where they can significantly enhance the diagnostic processes for genetic disorders, ultimately pushing the boundaries of personalized medicine by offering more detailed and actionable genetic insights. |
| title | Integrating Large Language Models for Genetic Variant Classification |
| topic | Genomics Artificial Intelligence Machine Learning 68T07 (Primary) 92D20, 92C40 (Secondary) I.2.7; J.3 |
| url | https://arxiv.org/abs/2411.05055 |