Identifying Patients with Atrioventricular Septal Defect in Down Syndrome Populations by Using Self-Normalizing Neural Networks and Feature Selection.
Identifying Patients with Atrioventricular Septal Defect in Down Syndrome Populations by Using Self-Normalizing Neural Networks and Feature Selection.
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通过使用自我标准化神经网络和特征选择来识别唐氏综合症人群中的房室间隔缺损患者
DOI：
10.3390/genes9040208
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发表时间：
2018-04-12
期刊：
影响因子：
3.5
通讯作者：
Cai YD
中科院分区：
文献类型：
--
作者：
Pan X;Hu X;Zhang YH;Feng K;Wang SP;Chen L;Huang T;Cai YD
房室间隔缺损（AVSD）是先天性心脏病（CHD）的一种临床重要亚型，严重影响婴儿出生时的健康，并与唐氏综合征（DS）有关。因此，探索有和无AVSD的DS样本中功能基因的差异是研究AVSD和DS之间复杂关联的关键方法。在这项研究中，我们提出了一种计算方法来区分DS患者与AVSD从那些没有AVSD使用新提出的自归一化神经网络（SNN）。首先，通过使用21号染色体上探针的拷贝数对每个患者进行编码。通过可靠的蒙特卡罗特征选择（MCFS）方法对编码特征进行排序，以获得排序特征列表。基于此特征列表，我们使用两阶段增量特征选择来构造两个系列的特征子集，并应用SNN来构建分类器以识别最佳特征。结果表明，2737个最佳功能，并相应的最佳SNN分类器上构建的最佳功能产生了马修的相关系数（MCC）值为0.748。为了比较，随机森林也被用来建立分类器和发现最佳功能。当利用前132个特征时，该方法获得了0.582的最佳MCC值。最后，我们分析了一些关键的特征派生的SNNs中的最佳功能，发现在文献支持，以进一步揭示它们的本质作用。
Atrioventricular septal defect (AVSD) is a clinically significant subtype of congenital heart disease (CHD) that severely influences the health of babies during birth and is associated with Down syndrome (DS). Thus, exploring the differences in functional genes in DS samples with and without AVSD is a critical way to investigate the complex association between AVSD and DS. In this study, we present a computational method to distinguish DS patients with AVSD from those without AVSD using the newly proposed self-normalizing neural network (SNN). First, each patient was encoded by using the copy number of probes on chromosome 21. The encoded features were ranked by the reliable Monte Carlo feature selection (MCFS) method to obtain a ranked feature list. Based on this feature list, we used a two-stage incremental feature selection to construct two series of feature subsets and applied SNNs to build classifiers to identify optimal features. Results show that 2737 optimal features were obtained, and the corresponding optimal SNN classifier constructed on optimal features yielded a Matthew’s correlation coefficient (MCC) value of 0.748. For comparison, random forest was also used to build classifiers and uncover optimal features. This method received an optimal MCC value of 0.582 when top 132 features were utilized. Finally, we analyzed some key features derived from the optimal features in SNNs found in literature support to further reveal their essential roles.
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作者：
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发表时间：
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影响因子：
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