Structured functional regression models for high-dimensional spatial spectroscopy data
Structured functional regression models for high-dimensional spatial spectroscopy data
复制标题
高维空间光谱数据的结构化函数回归模型
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
K. Shedden
中科院分区：
文献类型：
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作者：
A. Amini;E. Levina;K. Shedden
光谱数据的建模和分析是一个活跃的研究领域，应用于化学和生物学。本文重点分析从骨折愈合实验中获得的拉曼光谱，虽然预测从高维张量的标量响应的函数回归模型可以应用于任何光谱数据。回归模型建立在光谱的稀疏函数表示上，并适应多个空间维度。我们应用我们的模型预测骨矿物质密度（BMD），骨折愈合的一个重要指标，从拉曼光谱，在体内和体外设置的骨折愈合实验的任务。为了说明该方法的普遍适用性，我们也用它来预测脂蛋白浓度从核磁共振（NMR）光谱获得的光谱。
Modeling and analysis of spectroscopy data is an active area of research with applications to chemistry and biology. This paper focuses on analyzing Raman spectra obtained from a bone fracture healing experiment, although the functional regression model for predicting a scalar response from high-dimensional tensors can be applied to any spectroscopy data. The regression model is built on a sparse functional representation of the spectra, and accommodates multiple spatial dimensions. We apply our models to the task of predicting bone-mineral-density (BMD), an important indicator of fracture healing, from Raman spectra, in both the in vivo and ex vivo settings of the bone fracture healing experiment. To illustrate the general applicability of the method, we also use it to predict lipoprotein concentrations from spectra obtained by nuclear magnetic resonance (NMR) spectroscopy.