Cox-sMBPLS: An Algorithm for Disease Survival Prediction and Multi-Omics Module Discovery Incorporating Cis-Regulatory Quantitative Effects.
Cox-sMBPLS: An Algorithm for Disease Survival Prediction and Multi-Omics Module Discovery Incorporating Cis-Regulatory Quantitative Effects.
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DOI：
10.3389/fgene.2021.701405
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发表时间：
2021
影响因子：
3.7
通讯作者：
Michailidis G
中科院分区：
文献类型：
--
作者：
Vahabi N;McDonough CW;Desai AA;Cavallari LH;Duarte JD;Michailidis G
高通量技术的发展使得能够对许多分子隔间中的大量生物分子进行剖析。然后，挑战变成整合这种多模式OMICS数据，以获得对生物过程和疾病发生和发展机制的洞察。此外，鉴于这种数据的高维性，在开发用于数据集成的统计模型时纳入关于分子间相互作用的先前生物学信息是有益的，特别是在涉及少量样本的环境中。我们开发了事件间隔时间数据(例如，死亡、生化复发)的监督模型，该模型同时考虑了Omics配置文件中的冗余信息，并通过多块偏最小二乘框架利用了它们之间的先前生物关联。来自不同分子部分(如表观基因组、转录组、甲基组等)的数据之间的相互作用。在所提出的模型中，通过使用顺式调控数量效应来捕获。根据模拟研究和对心力衰竭患者数据的分析，该模型被命名为COX-sMBPLS，显示出优越的预测性能和改进的特征选择。该模型能有效地将先验生物信息融入到生存预测系统中，提高了预测性能和特征选择能力。它还能够识别影响患者生存概率的生物分子的多个Omics模块，并提供对值得进一步研究的潜在相关风险因素的洞察。
The development of high-throughput techniques has enabled profiling a large number of biomolecules across a number of molecular compartments. The challenge then becomes to integrate such multimodal Omics data to gain insights into biological processes and disease onset and progression mechanisms. Further, given the high dimensionality of such data, incorporating prior biological information on interactions between molecular compartments when developing statistical models for data integration is beneficial, especially in settings involving a small number of samples. We develop a supervised model for time to event data (e.g., death, biochemical recurrence) that simultaneously accounts for redundant information within Omics profiles and leverages prior biological associations between them through a multi-block PLS framework. The interactions between data from different molecular compartments (e.g., epigenome, transcriptome, methylome, etc.) were captured by using cis-regulatory quantitative effects in the proposed model. The model, coined Cox-sMBPLS, exhibits superior prediction performance and improved feature selection based on both simulation studies and analysis of data from heart failure patients. The proposed supervised Cox-sMBPLS model can effectively incorporate prior biological information in the survival prediction system, leading to improved prediction performance and feature selection. It also enables the identification of multi-Omics modules of biomolecules that impact the patients’ survival probability and also provides insights into potential relevant risk factors that merit further investigation.
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DOI：
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发表时间：
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期刊：
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