Language modelling techniques for analysing the impact of human genetic variation
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916652261572608 |
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| author | Hegde, Megha Nebel, Jean-Christophe Rahman, Farzana |
| author_facet | Hegde, Megha Nebel, Jean-Christophe Rahman, Farzana |
| contents | Interpreting the effects of variants within the human genome and proteome is essential for analysing disease risk, predicting medication response, and developing personalised health interventions. Due to the intrinsic similarities between the structure of natural languages and genetic sequences, natural language processing techniques have demonstrated great applicability in computational variant effect prediction. In particular, the advent of the Transformer has led to significant advancements in the field. However, Transformer-based models are not without their limitations, and a number of extensions and alternatives have been developed to improve results and enhance computational efficiency. This review explores the use of language models for computational variant effect prediction over the past decade, analysing the main architectures, and identifying key trends and future directions. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_10655 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Language modelling techniques for analysing the impact of human genetic variation Hegde, Megha Nebel, Jean-Christophe Rahman, Farzana Computation and Language Artificial Intelligence Biomolecules Interpreting the effects of variants within the human genome and proteome is essential for analysing disease risk, predicting medication response, and developing personalised health interventions. Due to the intrinsic similarities between the structure of natural languages and genetic sequences, natural language processing techniques have demonstrated great applicability in computational variant effect prediction. In particular, the advent of the Transformer has led to significant advancements in the field. However, Transformer-based models are not without their limitations, and a number of extensions and alternatives have been developed to improve results and enhance computational efficiency. This review explores the use of language models for computational variant effect prediction over the past decade, analysing the main architectures, and identifying key trends and future directions. |
| title | Language modelling techniques for analysing the impact of human genetic variation |
| topic | Computation and Language Artificial Intelligence Biomolecules |
| url | https://arxiv.org/abs/2503.10655 |