用语言模型分析基因变异影响，助力个性化医疗
Language modelling techniques for analysing the impact of human genetic variation
- 借鉴自然语言处理技术，将基因序列视为语言进行建模
- 回顾十年来主流架构，总结性能提升与计算效率优化路径
- 适合基因组学、AI医疗交叉研究者参考
解析人类基因组与蛋白质组中变异的影响，对疾病风险评估、药物反应预测和个性化健康干预至关重要。由于自然语言结构与遗传序列具有内在相似性，自然语言处理技术在计算变异效应预测中展现出广泛应用前景。特别是Transformer模型的出现，推动了该领域显著进展。然而，基于Transformer的模型仍存在局限，因此一系列改进与替代方案被提出以提升性能并增强计算效率。本文综述过去十年中语言模型在计算变异效应预测中的应用，分析主要模型架构，识别关键趋势与未来方向。
原文摘要 · Abstract (English)
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.
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