Genome-wide association study-based deep learning for survival prediction.
Genome-wide association study-based deep learning for survival prediction.
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DOI：
10.1002/sim.8743
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发表时间：
2020-12-30
影响因子：
2
通讯作者：
Ding Y
中科院分区：
文献类型：
--
作者：
Sun T;Wei Y;Chen W;Ding Y
随着时间的推移，个性化的动态风险特征提供信息和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。来自全基因组关联研究（GWAS）的大量遗传数据（如SNP）以及充分表征的事件发生时间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生物医学领域建立强大的预测模型方面取得了非凡的成就。然而，深度学习方法在生存预测中的应用是有限的，特别是在利用丰富的GWAS数据的情况下。为了开发针对眼部疾病年龄相关性黄斑变性（AMD）进展的强大预测模型，我们开发并实现了多层深度神经网络（DNN）生存模型，以有效地提取特征并做出准确和可解释的预测。进行了各种模拟研究，以比较DNN生存模型与其他几种基于机器学习的生存模型的预测性能。最后，使用来自两项AMD大规模随机临床试验的GWAS数据（超过7800个观察结果），我们表明DNN生存模型不仅在预测准确性方面优于几种现有的生存预测模型（例如，c-index =0.76），而且通过有效学习遗传变异之间的复杂结构，成功检测有临床意义的风险亚组。此外，我们从DNN生存模型中获得了每个预测因子的受试者特异性重要性指标，这为该疾病的个性化早期预防和临床管理提供了有价值的见解。
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.
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影响因子：
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