AESurv:用于准确早期预测冠心病的自动编码器生存分析。

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Briefings in bioinformatics Pub Date : 2024-09-23 DOI:10.1093/bib/bbae479
Yike Shen, Arce Domingo-Relloso, Allison Kupsco, Marianthi-Anna Kioumourtzoglou, Maria Tellez-Plaza, Jason G Umans, Amanda M Fretts, Ying Zhang, Peter F Schnatz, Ramon Casanova, Lisa Warsinger Martin, Steve Horvath, JoAnn E Manson, Shelley A Cole, Haotian Wu, Eric A Whitsel, Andrea A Baccarelli, Ana Navas-Acien, Feng Gao
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引用次数: 0

摘要

冠心病(CHD)是美国人死亡和发病的主要原因之一。具有高维DNA甲基化和临床特征的精确的冠心病时间到事件预测模型可能有助于早期预测和干预策略。我们开发了一种最先进的深度学习自动编码器生存分析模型(AESurv),通过学习参与者的低维表征,有效分析高维血液DNA甲基化特征和传统临床风险因素,从而进行时间到事件的冠心病预测。我们在两项队列研究中证明了我们的模型的实用性:强心研究队列(SHS)是一项前瞻性队列研究,研究对象是美国印第安人中的心血管疾病及其风险因素;妇女健康倡议(WHI)是一项前瞻性队列研究,包括随机临床试验和观察研究,旨在改善绝经后妇女的健康状况,重点之一是心血管疾病。与其他生存分析模型(Cox比例危险、Cox比例危险深度神经网络生存分析、随机生存森林和梯度提升生存分析模型)相比,我们的AESurv模型在SHS中有效地学习了低维潜在空间中的参与者表征,并取得了更好的模型性能(一致性指数-C指数为0.864 ± 0.009,时间-事件平均接收者操作特征曲线下面积-AUROC为0.905 ± 0.009)。我们在 WHI 中进一步验证了 AESurv 模型,也取得了最佳模型性能。AESurv 模型可用于准确预测心脏病,并帮助医护人员和患者实施早期干预策略。我们建议将来使用 AESurv 模型进行基于 DNA 甲基化特征的从时间到事件的心脏病预测。
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AESurv: autoencoder survival analysis for accurate early prediction of coronary heart disease.

Coronary heart disease (CHD) is one of the leading causes of mortality and morbidity in the United States. Accurate time-to-event CHD prediction models with high-dimensional DNA methylation and clinical features may assist with early prediction and intervention strategies. We developed a state-of-the-art deep learning autoencoder survival analysis model (AESurv) to effectively analyze high-dimensional blood DNA methylation features and traditional clinical risk factors by learning low-dimensional representation of participants for time-to-event CHD prediction. We demonstrated the utility of our model in two cohort studies: the Strong Heart Study cohort (SHS), a prospective cohort studying cardiovascular disease and its risk factors among American Indians adults; the Women's Health Initiative (WHI), a prospective cohort study including randomized clinical trials and observational study to improve postmenopausal women's health with one of the main focuses on cardiovascular disease. Our AESurv model effectively learned participant representations in low-dimensional latent space and achieved better model performance (concordance index-C index of 0.864 ± 0.009 and time-to-event mean area under the receiver operating characteristic curve-AUROC of 0.905 ± 0.009) than other survival analysis models (Cox proportional hazard, Cox proportional hazard deep neural network survival analysis, random survival forest, and gradient boosting survival analysis models) in the SHS. We further validated the AESurv model in WHI and also achieved the best model performance. The AESurv model can be used for accurate CHD prediction and assist health care professionals and patients to perform early intervention strategies. We suggest using AESurv model for future time-to-event CHD prediction based on DNA methylation features.

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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
期刊最新文献
Atomistic simulations reveal impacts of missense mutations on the structure and function of SynGAP1. COFFEE: consensus single cell-type specific inference for gene regulatory networks. DrugDoctor: enhancing drug recommendation in cold-start scenario via visit-level representation learning and training. 3t-seq: automatic gene expression analysis of single-copy genes, transposable elements, and tRNAs from RNA-seq data. AESurv: autoencoder survival analysis for accurate early prediction of coronary heart disease.
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