论文标题
一种基于信念网络的深层网络方法，用于识别阿尔茨海默氏病的蛋白质组学风险标志物
A deep belief network-based method to identify proteomic risk markers for Alzheimer disease
论文作者
论文摘要
尽管大量研究正式确定载脂蛋白E（APOE）是阿尔茨海默氏病的主要遗传风险标志物，但积累的证据支持了可能存在其他风险标志物的观念。但是，传统的阿尔茨海默氏症特异性签名分析方法无法完全使用丰富的蛋白质表达数据，尤其是属性之间的相互作用。本文开发了一种新型的特征选择方法，可以使用蛋白质组学和临床数据来鉴定阿尔茨海默氏病的致病因素。这种方法将网络节点的权重作为信号蛋白表达值的重要性顺序。在生成和评估候选子集后，该方法有助于选择获得大于90％的精度的最佳蛋白质子集，这比传统的机器学习方法优于临床阿尔茨海默氏病诊断。除了确定蛋白质组学风险标记并进一步增强了代谢危险因素与阿尔茨海默氏病之间的联系外，本文还表明，阿替元蛋白连接的途径是可能的治疗药物靶标。
While a large body of research has formally identified apolipoprotein E (APOE) as a major genetic risk marker for Alzheimer disease, accumulating evidence supports the notion that other risk markers may exist. The traditional Alzheimer-specific signature analysis methods, however, have not been able to make full use of rich protein expression data, especially the interaction between attributes. This paper develops a novel feature selection method to identify pathogenic factors of Alzheimer disease using the proteomic and clinical data. This approach has taken the weights of network nodes as the importance order of signaling protein expression values. After generating and evaluating the candidate subset, the method helps to select an optimal subset of proteins that achieved an accuracy greater than 90%, which is superior to traditional machine learning methods for clinical Alzheimer disease diagnosis. Besides identifying a proteomic risk marker and further reinforce the link between metabolic risk factors and Alzheimer disease, this paper also suggests that apidonectin-linked pathways are a possible therapeutic drug target.
