A regularized Cox hierarchical model for incorporating annotation information in predictive omic studies.

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Biodata Mining Pub Date : 2024-10-24 DOI:10.1186/s13040-024-00398-6
Dixin Shen, Juan Pablo Lewinger, Eric Kawaguchi
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Abstract

Background: Associated with high-dimensional omics data there are often "meta-features" such as biological pathways and functional annotations, summary statistics from similar studies that can be informative for predicting an outcome of interest. We introduce a regularized hierarchical framework for integrating meta-features, with the goal of improving prediction and feature selection performance with time-to-event outcomes.

Methods: A hierarchical framework is deployed to incorporate meta-features. Regularization is applied to the omic features as well as the meta-features so that high-dimensional data can be handled at both levels. The proposed hierarchical Cox model can be efficiently fitted by a combination of iterative reweighted least squares and cyclic coordinate descent.

Results: In a simulation study we show that when the external meta-features are informative, the regularized hierarchical model can substantially improve prediction performance over standard regularized Cox regression. We illustrate the proposed model with applications to breast cancer and melanoma survival based on gene expression profiles, which show the improvement in prediction performance by applying meta-features, as well as the discovery of important omic feature sets with sparse regularization at meta-feature level.

Conclusions: The proposed hierarchical regularized regression model enables integration of external meta-feature information directly into the modeling process for time-to-event outcomes, improves prediction performance when the external meta-feature data is informative. Importantly, when the external meta-features are uninformative, the prediction performance based on the regularized hierarchical model is on par with standard regularized Cox regression, indicating robustness of the framework. In addition to developing predictive signatures, the model can also be deployed in discovery applications where the main goal is to identify important features associated with the outcome rather than developing a predictive model.

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将注释信息纳入预测性 omic 研究的正则化 Cox 层次模型。
背景:与高维 omics 数据相关的往往是 "元特征",如生物通路和功能注释,这些来自类似研究的总结性统计数据可能对预测感兴趣的结果具有参考价值。我们引入了一个正则化的分层框架来整合元特征,目的是提高时间到事件结果的预测和特征选择性能:方法:采用分层框架整合元特征。方法:采用分层框架纳入元特征,并对omic特征和元特征进行正则化处理,从而在两个层面上处理高维数据。结合迭代加权最小二乘法和循环坐标下降法,可以有效拟合所提出的分层考克斯模型:在一项模拟研究中,我们发现当外部元特征信息丰富时,正则化分层模型比标准正则化 Cox 回归能大幅提高预测性能。我们将提出的模型应用于基于基因表达谱的乳腺癌和黑色素瘤存活率研究,结果表明,应用元特征可以提高预测性能,在元特征水平上进行稀疏正则化还可以发现重要的 omic 特征集:结论:所提出的分层正则化回归模型能将外部元特征信息直接整合到时间到事件结果的建模过程中,当外部元特征数据信息丰富时,能提高预测性能。重要的是,当外部元特征信息不丰富时,基于正则化分层模型的预测性能与标准正则化 Cox 回归相当,这表明了该框架的稳健性。除了开发预测特征外,该模型还可以部署在发现应用中,其主要目标是识别与结果相关的重要特征,而不是开发预测模型。
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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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