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首页> 外文会议>IEEE International Symposium on Biomedical Imaging >Deep Network-Based Feature Selection for Imaging Genetics: Application to Identifying Biomarkers for Parkinson's Disease
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Deep Network-Based Feature Selection for Imaging Genetics: Application to Identifying Biomarkers for Parkinson's Disease

机译：基于深度网络的成像遗传学特征选择：在帕金森氏病生物标志物识别中的应用

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摘要

Imaging genetics is a methodology for discovering associations between imaging and genetic variables. Many studies adopted sparse models such as sparse canonical correlation analysis (SCCA) for imaging genetics. These methods are limited to modeling the linear imaging genetics relationship and cannot capture the non-linear high-level relationship between the explored variables. Deep learning approaches are underexplored in imaging genetics, compared to their great successes in many other biomedical domains such as image segmentation and disease classification. In this work, we proposed a deep learning model to select genetic features that can explain the imaging features well. Our empirical study on simulated and real datasets demonstrated that our method outperformed the widely used SCCA method and was able to select important genetic features in a robust fashion. These promising results indicate our deep learning model has the potential to reveal new biomarkers to improve mechanistic understanding of the studied brain disorders.
机译：成像遗传学是一种发现成像与遗传变量之间关联的方法。许多研究采用稀疏模型，例如稀疏典范相关分析（SCCA）来成像遗传学。这些方法仅限于对线性成像遗传关系进行建模，而无法捕获所探查变量之间的非线性高级关系。与在许多其他生物医学领域（例如图像分割和疾病分类）中取得的巨大成功相比，深度学习方法在成像遗传学方面的研究还很不足。在这项工作中，我们提出了一个深度学习模型来选择可以很好地解释成像特征的遗传特征。我们对模拟和真实数据集的实证研究表明，我们的方法优于广泛使用的SCCA方法，并且能够以鲁棒的方式选择重要的遗传特征。这些有希望的结果表明，我们的深度学习模型有可能揭示新的生物标志物，以改善对研究的脑部疾病的机械理解。

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