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Genome-wide association study-based deep learning for survival prediction.
Statistics in Medicine ( IF 2 ) Pub Date : 2020-09-24 , DOI: 10.1002/sim.8743
Tao Sun 1, 2 , Yue Wei 1 , Wei Chen 1, 3 , Ying Ding 1
Affiliation  

Informative and accurate survival prediction with individualized dynamic risk profiles over time is critical for personalized disease prevention and clinical management. The massive genetic data, such as SNPs from genome‐wide association studies (GWAS), together with well‐characterized time‐to‐event phenotypes provide unprecedented opportunities for developing effective survival prediction models. Recent advances in deep learning have made extraordinary achievements in establishing powerful prediction models in the biomedical field. However, the applications of deep learning approaches in survival prediction are limited, especially with utilizing the wealthy GWAS data. Motivated by developing powerful prediction models for the progression of an eye disease, age‐related macular degeneration (AMD), we develop and implement a multilayer deep neural network (DNN) survival model to effectively extract features and make accurate and interpretable predictions. Various simulation studies are performed to compare the prediction performance of the DNN survival model with several other machine learning‐based survival models. Finally, using the GWAS data from two large‐scale randomized clinical trials in AMD with over 7800 observations, we show that the DNN survival model not only outperforms several existing survival prediction models in terms of prediction accuracy (eg, c‐index =0.76), but also successfully detects clinically meaningful risk subgroups by effectively learning the complex structures among genetic variants. Moreover, we obtain a subject‐specific importance measure for each predictor from the DNN survival model, which provides valuable insights into the personalized early prevention and clinical management for this disease.

中文翻译:

基于全基因组关联研究的深度学习,用于生存预测。

随时间推移具有个性化动态风险特征的准确,准确的生存预测对于个性化疾病预防和临床管理至关重要。大量的遗传数据,例如来自全基因组关联研究(GWAS)的SNP,以及特征明确的时间事件表型,为开发有效的生存预测模型提供了前所未有的机会。深度学习的最新进展在建立生物医学领域强大的预测模型方面取得了非凡的成就。但是,深度学习方法在生存预测中的应用是有限的,尤其是在利用丰富的GWAS数据的情况下。通过开发针对眼疾,年龄相关性黄斑变性(AMD)进展的强大预测模型,我们开发并实施了多层深度神经网络(DNN)生存模型,以有效提取特征并做出准确且可解释的预测。进行了各种模拟研究,以将DNN生存模型的预测性能与其他几种基于机器学习的生存模型进行比较。最后,使用来自AMD的两项大规模随机临床试验中的GWAS数据并进行了7800多次观察,我们发现DNN生存模型不仅在预测准确性方面优于现有的几种生存预测模型(例如,c指数)= 0.76),而且还可以通过有效地学习遗传变异中的复杂结构来成功地检测出具有临床意义的风险亚组。此外,我们从DNN生存模型中为每个预测变量获得了特定于主题的重要性度量,这为对该疾病的个性化早期预防和临床管理提供了宝贵的见解。
更新日期:2020-09-24
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