Prioritization of risk genes for Alzheimer's disease: an analysis framework using spatial and temporal gene expression data in the human brain based on support vector machine
Prioritization of risk genes for Alzheimer's disease: an analysis framework using spatial and temporal gene expression data in the human brain based on support vector machine
复制标题
阿尔茨海默病风险基因的优先排序：基于支持向量机的人脑时空基因表达数据的分析框架
DOI：
10.1101/2023.02.06.23285522
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Tianxiao Zhang
中科院分区：
文献类型：
--
作者：
Shiyu Wang;Xixian Fang;Xiang Wen;Congying Yang;Ying Yang;Tianxiao Zhang
背景：阿尔茨海默病（AD）是一种复杂的疾病，其发病风险受多种遗传和环境因素的影响。本研究提出了一种基于基因表达数据时空特征（STGE）的AD风险基因预测框架。方法：基于基因表达数据的时空特征，构建AD风险基因预测框架。以不同组织和年龄供体的基因表达数据作为模型特征。根据从相关数据库中提取的信息，将人类基因分为AD风险组和非风险组。构建支持向量机（SVM）模型，捕捉与AD风险相关的基因表达模式。结果：采用递归特征消除（RFE）方法进行特征选择。特征选择前共获得64个组织年龄特征数据，RFE后减少到19个。使用19个选定的完整特征构建SVM模型并对其进行评估。基于19个选定特征（0.740[0.690-0.790]）和完整特征集（0.730[0.678-0.769]）的SVM模型的曲线下面积（AUC）值非常相似。15个基因被预测为阿尔茨海默病的风险基因，其概率大于90%。结论：新提出的框架与先前基于蛋白质-蛋白质相互作用（PPI）网络特性的预测方法相比具有可比性。生成了15个AD风险候选基因，为进一步研究AD的遗传病因提供数据支持。
Background:Alzheimer’s disease (AD) is a complex disorder, and its risk is influenced by multiple genetic and environmental factors. In this study, an AD risk gene prediction framework based on spatial and temporal features of gene expression data (STGE) was proposed.Methods:We proposed an AD risk gene prediction framework based on spatial and temporal features of gene expression data. The gene expression data of providers of different tissues and ages were used as model features. Human genes were classified as AD risk or non-risk sets based on information extracted from relevant databases. Support vector machine (SVM) models were constructed to capture the expression patterns of genes believed to contribute to the risk of AD.Results:The recursive feature elimination (RFE) method was utilized for feature selection. Data for 64 tissue-age features were obtained before feature selection, and this number was reduced to 19 after RFE was performed. The SVM models were built and evaluated using 19 selected and full features. The area under curve (AUC) values for the SVM model based on 19 selected features (0.740 [0.690–0.790]) and full feature sets (0.730 [0.678–0.769]) were very similar. Fifteen genes predicted to be risk genes for AD with a probability greater than 90% were obtained.Conclusion:The newly proposed framework performed comparably to previous prediction methods based on protein-protein interaction (PPI) network properties. A list of 15 candidate genes for AD risk was also generated to provide data support for further studies on the genetic etiology of AD.