Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan, 430070, China.
Hubei Hongshan Laboratory, Wuhan, 430070, China.
Adv Sci (Weinh). 2024 Apr;11(14):e2304842. doi: 10.1002/advs.202304842. Epub 2024 Feb 2.
The identification and classification of selective sweeps are of great significance for improving the understanding of biological evolution and exploring opportunities for precision medicine and genetic improvement. Here, a domain adaptation sweep detection and classification (DASDC) method is presented to balance the alignment of two domains and the classification performance through a domain-adversarial neural network and its adversarial learning modules. DASDC effectively addresses the issue of mismatch between training data and real genomic data in deep learning models, leading to a significant improvement in its generalization capability, prediction robustness, and accuracy. The DASDC method demonstrates improved identification performance compared to existing methods and excels in classification performance, particularly in scenarios where there is a mismatch between application data and training data. The successful implementation of DASDC in real data of three distinct species highlights its potential as a useful tool for identifying crucial functional genes and investigating adaptive evolutionary mechanisms, particularly with the increasing availability of genomic data.
选择清除的识别和分类对于提高对生物进化的理解以及探索精准医学和遗传改良的机会具有重要意义。在这里，提出了一种域自适应清除检测和分类（DASDC）方法，通过域对抗神经网络及其对抗学习模块来平衡两个域的对齐和分类性能。DASDC 有效地解决了深度学习模型中训练数据与真实基因组数据之间不匹配的问题，从而显著提高了其泛化能力、预测稳健性和准确性。与现有方法相比，DASDC 方法在识别性能方面有所提高，在应用数据与训练数据不匹配的情况下，分类性能表现尤为出色。DASDC 在三个不同物种的真实数据中的成功实施突显了其作为识别关键功能基因和研究适应性进化机制的有用工具的潜力，特别是随着基因组数据的日益丰富。
