Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), has revolutionized medical research, facilitating advancements in drug discovery and cancer diagnosis. ML identifies patterns in data, while DL employs neural networks for intricate processing. Predictive modeling challenges, such as data labeling, are addressed by transfer learning (TL), leveraging pre-existing models for faster training. TL shows potential in genetic research, improving tasks like gene expression analysis, mutation detection, genetic syndrome recognition, and genotype–phenotype association. This review explores the role of TL in overcoming challenges in mutation detection, genetic syndrome detection, gene expression, or phenotype–genotype association. TL has shown effectiveness in various aspects of genetic research. TL enhances the accuracy and efficiency of mutation detection, aiding in the identification of genetic abnormalities. TL can improve the diagnostic accuracy of syndrome-related genetic patterns. Moreover, TL plays a crucial role in gene expression analysis in order to accurately predict gene expression levels and their interactions. Additionally, TL enhances phenotype–genotype association studies by leveraging pre-trained models. In conclusion, TL enhances AI efficiency by improving mutation prediction, gene expression analysis, and genetic syndrome detection. Future studies should focus on increasing domain similarities, expanding databases, and incorporating clinical data for better predictions.
人工智能（AI），包括机器学习（ML）和深度学习（DL），已彻底改变医学研究，推动了药物发现和癌症诊断的进步。机器学习通过识别数据中的模式发挥作用，而深度学习则利用神经网络进行复杂处理。针对数据标注等预测建模挑战，迁移学习（TL）通过利用预训练模型加速训练过程提供了解决方案。迁移学习在遗传研究中展现出巨大潜力，能够改进基因表达分析、突变检测、遗传综合征识别以及基因型-表型关联分析等任务。本综述探讨了迁移学习在克服突变检测、遗传综合征识别、基因表达分析及表型-基因型关联研究等挑战中的作用。研究表明，迁移学习在遗传研究的多个方面均表现出显著效果：它提升了突变检测的准确性和效率，有助于识别遗传异常；改善了综合征相关遗传模式的诊断精度；在基因表达分析中发挥关键作用，可精准预测基因表达水平及其相互作用；同时通过利用预训练模型增强了表型-基因型关联研究的效能。综上所述，迁移学习通过优化突变预测、基因表达分析和遗传综合征检测，显著提升了人工智能系统的效率。未来研究应着重增强领域相似性、扩展数据库规模并整合临床数据，以实现更精准的预测效果。
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