A Novel Approach Utilizing Domain Adversarial Neural Networks for the Detection and Classification of Selective Sweeps

文献类型： 外文期刊

第一作者： Song, Hui

作者： Song, Hui;Chu, Jinyu;Li, Wangjiao;Li, Xinyun;Han, Jianlin;Zhao, Shuhong;Ma, Yunlong;Song, Hui;Chu, Jinyu;Li, Wangjiao;Li, Xinyun;Han, Jianlin;Zhao, Shuhong;Ma, Yunlong;Li, Xinyun;Zhao, Shuhong;Ma, Yunlong;Fang, Lingzhao;Han, Jianlin;Han, Jianlin;Zhao, Shuhong;Ma, Yunlong

作者机构：

关键词： domain adaptation; deep learning; genomics; robustness; selective sweep

期刊名称：ADVANCED SCIENCE （ 影响因子：15.1； 五年影响因子：16.7 ）

ISSN：

年卷期： 2024 年 11 卷 14 期

页码：

收录情况： SCI

摘要： 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. This study investigates the risk of using simulated genomic data to train deep learning models for selective sweep detection. To address this, the study introduces a domain adaptation strategy and develops the Domain Adaptation Sweep Detection and Classification (DASDC) method. The results suggest that DASDC performs well in multispecies applications and demonstrates significant improvements in prediction robustness and accuracy. image

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