IEEE/ACM Trans Comput Biol Bioinform. 2021 Nov-Dec;18(6):2281-2290. doi: 10.1109/TCBB.2021.3053061. Epub 2021 Dec 8.
Mild Cognitive Impairment (MCI) is a preclinical stage of Alzheimer's Disease (AD) and is clinical heterogeneity. The classification of MCI is crucial for the early diagnosis and treatment of AD. In this study, we investigated the potential of using both labeled and unlabeled samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort to classify MCI through the multimodal co-training method. We utilized both structural magnetic resonance imaging (sMRI) data and genotype data of 364 MCI samples including 228 labeled and 136 unlabeled MCI samples from the ADNI-1 cohort. First, the selected quantitative trait (QT) features from sMRI data and SNP features from genotype data were used to build two initial classifiers on 228 labeled MCI samples. Then, the co-training method was implemented to obtain new labeled samples from 136 unlabeled MCI samples. Finally, the random forest algorithm was used to obtain a combined classifier to classify MCI patients in the independent ADNI-2 dataset. The experimental results showed that our proposed framework obtains an accuracy of 85.50 percent and an AUC of 0.825 for MCI classification, respectively, which showed that the combined utilization of sMRI and SNP data through the co-training method could significantly improve the performances of MCI classification.
轻度认知障碍 (MCI) 是阿尔茨海默病 (AD) 的临床前阶段，具有临床异质性。MCI 的分类对于 AD 的早期诊断和治疗至关重要。在这项研究中，我们通过多模态协同训练方法，研究了使用来自阿尔茨海默病神经影像学倡议 (ADNI) 队列的有标记和无标记样本对 MCI 进行分类的潜力。我们利用来自 ADNI-1 队列的 364 例 MCI 样本的结构磁共振成像 (sMRI) 数据和基因型数据，包括 228 例有标记和 136 例无标记 MCI 样本。首先，从 sMRI 数据中选择定量性状 (QT) 特征和基因型数据中的 SNP 特征，在 228 例有标记的 MCI 样本上构建两个初始分类器。然后，实施协同训练方法，从 136 例无标记的 MCI 样本中获得新的有标记样本。最后，使用随机森林算法获得组合分类器，以对独立的 ADNI-2 数据集进行 MCI 患者分类。实验结果表明，我们提出的框架分别在 MCI 分类中获得了 85.50%的准确率和 0.825 的 AUC，这表明通过协同训练方法联合使用 sMRI 和 SNP 数据可以显著提高 MCI 分类的性能。
