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首页> 外文期刊>Bioinformatics >Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learning
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Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learning

机译：通过稀疏多模式多任务学习从多维异质成像遗传学数据中识别疾病敏感和与数量相关的与特征相关的生物标记

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摘要

Motivation: Recent advances in brain imaging and high-throughput genotyping techniques enable new approaches to study the influence of genetic and anatomical variations on brain functions and disorders. Traditional association studies typically perform independent and pairwise analysis among neuroimaging measures, cognitive scores and disease status, and ignore the important underlying interacting relationships between these units. Results: To overcome this limitation, in this article, we propose a new sparse multimodal multitask learning method to reveal complex relationships from gene to brain to symptom. Our main contributions are three-fold: (i) introducing combined structured sparsity regularizations into multimodal multitask learning to integrate multidimensional heterogeneous imaging genetics data and identify multimodal biomarkers; (ii) utilizing a joint classification and regression learning model to identify disease-sensitive and cognition-relevant biomarkers; (iii) deriving a new efficient optimization algorithm to solve our non-smooth objective function and providing rigorous theoretical analysis on the global optimum convergency. Using the imaging genetics data from the Alzheimer's Disease Neuroimaging Initiative database, the effectiveness of the proposed method is demonstrated by clearly improved performance on predicting both cognitive scores and disease status. The identified multimodal biomarkers could predict not only disease status but also cognitive function to help elucidate the biological pathway from gene to brain structure and function, and to cognition and disease.
机译：动机：脑成像和高通量基因分型技术的最新进展为研究遗传和解剖变异对脑功能和疾病的影响提供了新方法。传统的关联研究通常在神经影像测量，认知评分和疾病状态之间执行独立且成对的分析，而忽略这些单元之间重要的潜在相互作用关系。结果：为克服这一局限性，本文提出了一种新的稀疏多峰多任务学习方法，以揭示从基因到大脑再到症状的复杂关系。我们的主要贡献有三方面：（i）将组合的结构化稀疏性正则化引入多模态多任务学习中，以整合多维异构成像遗传学数据并识别多模态生物标记; （ii）利用联合分类和回归学习模型来识别对疾病敏感和与认知有关的生物标志物; （iii）推导新的高效优化算法来求解我们的非光滑目标函数，并对全局最优收敛性进行严格的理论分析。使用来自阿尔茨海默氏病神经影像学计划数据库的影像学遗传学数据，通过明显提高了预测认知评分和疾病状态的性能，证明了该方法的有效性。鉴定出的多峰生物标志物不仅可以预测疾病状态，还可以预测认知功能，以帮助阐明从基因到大脑结构和功能，再到认知和疾病的生物学途径。

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