Diagnostic AI Modeling and Pseudo Time Series Profiling of AD and PD Based on Individualized Serum Proteome Data.
Diagnostic AI Modeling and Pseudo Time Series Profiling of AD and PD Based on Individualized Serum Proteome Data.
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基于个性化血清蛋白质组数据的 AD 和 PD 诊断 AI 建模和伪时间序列分析
DOI：
10.3389/fbinf.2021.764497
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Hu, Zhiyuan
中科院分区：
文献类型：
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作者：
Zhang, Jianhu;Zhang, Xiuli;Sh, Yuan;Liu, Benliang;Hu, Zhiyuan
背景：帕金森病（PD）、阿尔茨海默病（AD）是常见的神经退行性疾病，而轻度认知障碍（MCI）可能发生在AD或PD的早期。血液生物标志物被认为具有侵入性小、成本低、方便等优点，在神经退行性疾病的诊断和预测方面具有巨大的潜力。人工智能（AI）作为一个最近被提及的领域，经常被应用于生物学，并显示出优异的效果。在本文中，我们使用AI对PD、AD、MCI数据进行建模，并分析它们之间可能存在的联系。方法：从Gene Expression Omnibus （GEO）采集156例CT、50例MCI、132例PD、50例AD的人血蛋白微阵列图谱。首先，利用生物信息学方法和机器学习中的特征工程筛选重要特征，基于这些特征构建人工神经网络（ANN）分类器模型进行样本区分，并以分类精度和曲线下面积（AUC）评价模型的性能。其次，我们采用匠心途径分析（Ingenuity Pathway Analysis， IPA）方法，通过预测上游调控因子，分析不同疾病早期和晚期样本的途径和功能，以及药物干预的潜在靶点。结果：我们使用不同的分类器构建模型，最终发现人工神经网络模型优于传统的机器学习模型。综上所述，我们构建了三种不同的分类器，用于不同的应用场景。首先，我们纳入了EPHA2、MRPL19、SGK2等6个指标，构建了AD的诊断模型，测试集准确率高达98.07%。其次，纳入ERO1LB、FAM73B、IL1RN等15项指标，构建PD诊断模型，测试集准确率为97.05%。结合XG、FGFR3、CDC37等15项指标，建立AD和PD的四类诊断模型，测试集准确率为98.71%。所有分类器模型的auc值都大于0.95。然后，我们验证了构建的特征工程过滤掉了更少的重要特征，但包含了更多的信息，这有助于构建更好的模型。此外，通过更仔细地将疾病类型分别分为AD、MCI和PD的早期和晚期，我们发现早期PD可能比早期MCI发生得更早。最后，有24种蛋白在疾病组与正常组中既是差异表达蛋白又是上游调控蛋白，这些蛋白可能作为潜在的治疗靶点和后续研究的靶点。结论：我们构建的特征工程可以在减少特征数量的同时更好地提取信息，这可能有助于后续的应用。利用深度学习方法构建基于血液蛋白谱的分类器可以获得更好的分类性能，并且可以帮助我们早期诊断疾病。总之，从诊断和介入两方面研究神经退行性疾病对我们很重要。
Background: Parkinson’s disease (PD), Alzheimer’s disease (AD) are common neurodegenerative disease, while mild cognitive impairment (MCI) may be happened in the early stage of AD or PD. Blood biomarkers are considered to be less invasive, less cost and more convenient, and there is tremendous potential for the diagnosis and prediction of neurodegenerative diseases. As a recently mentioned field, artificial intelligence (AI) is often applied in biology and shows excellent results. In this article, we use AI to model PD, AD, MCI data and analyze the possible connections between them. Method: Human blood protein microarray profiles including 156 CT, 50 MCI, 132 PD, 50 AD samples are collected from Gene Expression Omnibus (GEO). First, we used bioinformatics methods and feature engineering in machine learning to screen important features, constructed artificial neural network (ANN) classifier models based on these features to distinguish samples, and evaluated the model’s performance with classification accuracy and Area Under Curve (AUC). Second, we used Ingenuity Pathway Analysis (IPA) methods to analyse the pathways and functions in early stage and late stage samples of different diseases, and potential targets for drug intervention by predicting upstream regulators. Result: We used different classifier to construct the model and finally found that ANN model would outperform the traditional machine learning model. In summary, three different classifiers were constructed to be used in different application scenarios, First, we incorporated 6 indicators, including EPHA2, MRPL19, SGK2, to build a diagnostic model for AD with a test set accuracy of up to 98.07%. Secondly, incorporated 15 indicators such as ERO1LB, FAM73B, IL1RN to build a diagnostic model for PD, with a test set accuracy of 97.05%. Then, 15 indicators such as XG, FGFR3 and CDC37 were incorporated to establish a four-category diagnostic model for both AD and PD, with a test set accuracy of 98.71%. All classifier models have an auc value greater than 0.95. Then, we verified that the constructed feature engineering filtered out fewer important features but contained more information, which helped to build a better model. In addition, by classifying the disease types more carefully into early and late stages of AD, MCI, and PD, respectively, we found that early PD may occur earlier than early MCI. Finally, there are 24 proteins that are both differentially expressed proteins and upstream regulators in the disease group versus the normal group, and these proteins may serve as potential therapeutic targets and targets for subsequent studies. Conclusion: The feature engineering we build allows better extraction of information while reducing the number of features, which may help in subsequent applications. Building a classifier based on blood protein profiles using deep learning methods can achieve better classification performance, and it can help us to diagnose the disease early. Overall, it is important for us to study neurodegenerative diseases from both diagnostic and interventional aspects.