Pairwise Correlation Analysis of the Alzheimer's Disease Neuroimaging Initiative (ADNI) Dataset Reveals Significant Feature Correlation.
Pairwise Correlation Analysis of the Alzheimer's Disease Neuroimaging Initiative (ADNI) Dataset Reveals Significant Feature Correlation.
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DOI：
10.3390/genes12111661
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发表时间：
2021-10-21
期刊：
影响因子：
3.5
通讯作者：
Miller JB
中科院分区：
文献类型：
--
作者：
Huckvale ED;Hodgman MW;Greenwood BB;Stucki DO;Ward KM;Ebbert MTW;Kauwe JSK;The Alzheimer's Disease Neuroimaging Initiative;The Alzheimer's Disease Metabolomics Consortium;Miller JB
阿尔茨海默病神经成像倡议(ADNI)包含广泛的患者测量(例如，磁共振成像[MRI]、生物识别、RNA表达等)。来自阿尔茨海默病(AD)病例和对照，最近机器学习算法使用这些病例和对照来评估AD的发病和进展。虽然使用各种生物标志物对AD研究至关重要，但高度相关的输入特征会显著降低机器学习模型的泛化能力和性能。此外，冗余特征不必要地增加了训练预测模型所需的计算时间和资源。因此，我们使用了49,288个生物标记物和793,600个提取的MRI特征来评估ADNI数据集中的特征相关性，以确定这个问题可能影响使用这些数据的大规模分析的程度。我们发现93.457%的生物标记物、92.549%的基因表达值和100%的磁共振特征与我们的Bonferroni校正α(p-Value≤1.40754×10−13)中的至少一个其他特征有很强的相关性。我们提供所有ADNI生物标记物与数据集中高度相关的特征的全面映射。此外，我们还指出，在执行批量数据分析之前，应解决ADNI数据集中的显著相关性，并提供解决这些问题的建议。我们预计，这些建议和资源将有助于指导研究人员利用ADNI数据集来提高模型性能，并降低分析的成本和复杂性。
The Alzheimer’s Disease Neuroimaging Initiative (ADNI) contains extensive patient measurements (e.g., magnetic resonance imaging [MRI], biometrics, RNA expression, etc.) from Alzheimer’s disease (AD) cases and controls that have recently been used by machine learning algorithms to evaluate AD onset and progression. While using a variety of biomarkers is essential to AD research, highly correlated input features can significantly decrease machine learning model generalizability and performance. Additionally, redundant features unnecessarily increase computational time and resources necessary to train predictive models. Therefore, we used 49,288 biomarkers and 793,600 extracted MRI features to assess feature correlation within the ADNI dataset to determine the extent to which this issue might impact large scale analyses using these data. We found that 93.457% of biomarkers, 92.549% of the gene expression values, and 100% of MRI features were strongly correlated with at least one other feature in ADNI based on our Bonferroni corrected α (p-value ≤ 1.40754 × 10−13). We provide a comprehensive mapping of all ADNI biomarkers to highly correlated features within the dataset. Additionally, we show that significant correlation within the ADNI dataset should be resolved before performing bulk data analyses, and we provide recommendations to address these issues. We anticipate that these recommendations and resources will help guide researchers utilizing the ADNI dataset to increase model performance and reduce the cost and complexity of their analyses.
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影响因子：
4.6
作者：
Oyama, Akane;Takeda, Shuko;Morishita, Ryuichi
通讯作者：
Morishita, Ryuichi
影响因子：
6.3
作者：
Basney, J;Livny, M;Mazzanti, P
通讯作者：
Mazzanti, P
DOI：
10.1186/alzrt155
发表时间：
2013
期刊：
Alzheimer's research & therapy
影响因子：
--
作者：
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通讯作者：
Black SE
影响因子：
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作者：
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通讯作者：
Ritchie, Craig W.
影响因子：
3.2
作者：
Besser, Lilah M.;Mock, Charles;Crary, John F.
通讯作者：
Crary, John F.