用深度学习降噪并挖掘致病基因特征，提升遗传病诊断准确率。
Machine Learning Algorithm for Noise Reduction and Disease-Causing Gene Feature Extraction in Gene Sequencing Data
- 融合CNN与RNN的DeepSeqDenoise算法有效降噪
- 信号噪声比提升9.4 dB，预测致病基因准确率达94.3%
- 发现57个新候选致病基因，临床检测出3个漏诊变异
本研究提出一种基于机器学习的基因测序数据降噪与致病基因特征提取方法。DeepSeqDenoise算法结合卷积神经网络（CNN）与循环神经网络（RNN），有效去除测序噪声，将信噪比提升9.4 dB。通过特征工程筛选出17个关键特征，并构建集成学习模型，对致病基因的预测准确率达到94.3%。在心血管疾病队列验证中成功识别57个新的候选致病基因，并在临床应用中检测出3个被遗漏的致病变异。该方法显著优于现有工具，为遗传病精准诊断提供了有力支持。
原文摘要 · Abstract (English)
In this study, we propose a machine learning-based method for noise reduction and disease-causing gene feature extraction in gene sequencing DeepSeqDenoise algorithm combines CNN and RNN to effectively remove the sequencing noise, and improves the signal-to-noise ratio by 9.4 dB. We screened 17 key features by feature engineering, and constructed an integrated learning model to predict disease-causing genes with 94.3% accuracy. We successfully identified 57 new candidate disease-causing genes in a cardiovascular disease cohort validation, and detected 3 missed variants in clinical applications. The method significantly outperforms existing tools and provides strong support for accurate diagnosis of genetic diseases.
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