Identifying Patients with Atrioventricular Septal Defect in Down Syndrome Populations by Using Self-Normalizing Neural Networks and Feature Selection
文献类型：期刊论文
| 作者 | Pan, Xiaoyong2,3; Wang, Shao Peng2; Cai, Yu Dong2; Hu, Xiaohua4; Zhang, Yu Hang5; Huang, Tao5; Feng, Kaiyan1; Chen, Lei6; , |
| 刊名 | GENES
 |
| 出版日期 | 2018 |
| 卷号 | 9期号:4页码:208 |
| 关键词 | atrioventricular septal defect Down syndrome self-normalizing neural network Monte Carlo feature selection random forest |
| ISSN号 | 2073-4425 |
| DOI | 10.3390/genes9040208 |
| 文献子类 | Article |
| 英文摘要 | 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. |
| 学科主题 | Genetics & Heredity |
| WOS关键词 | CONGENITAL HEART-DISEASE ; COPY-NUMBER VARIATION ; MENTAL-RETARDATION ; SCIENTIFIC STATEMENT ; PROTEIN INTERACTIONS ; GENE ; PREDICTION ; EXPRESSION ; CLASSIFICATION ; PHOSPHODIESTERASE |
| 语种 | 英语 |
| CSCD记录号 | CSCD:31763980 |
| WOS记录号 | WOS:000435182200036 |
| 出版者 | MDPI |
| 版本 | 出版稿 |
| 源URL | [http://202.127.25.144/handle/331004/774]  |
| 专题 | 中国科学院上海生命科学研究院营养科学研究所 |
| 作者单位 | 1.Guangdong AIB Polytech, Dept Comp Sci, Guangzhou 510507, Guangdong, Peoples R China; 2.Shanghai Univ, Coll Life Sci, Shanghai 200444, Peoples R China; 3.Erasmus MC, Dept Med Informat, NL-3015 CE Rotterdam, Netherlands; 4.Fudan Univ, Sch Life Sci, Dept Biostat & Computat Biol, Shanghai 200438, Peoples R China; 5.Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Hlth Sci, Shanghai 200031, Peoples R China; 6.Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China, |
| 推荐引用方式 GB/T 7714 | Pan, Xiaoyong,Wang, Shao Peng,Cai, Yu Dong,et al. Identifying Patients with Atrioventricular Septal Defect in Down Syndrome Populations by Using Self-Normalizing Neural Networks and Feature Selection[J]. GENES,2018,9(4):208. |
| APA | Pan, Xiaoyong.,Wang, Shao Peng.,Cai, Yu Dong.,Hu, Xiaohua.,Zhang, Yu Hang.,...&,.(2018).Identifying Patients with Atrioventricular Septal Defect in Down Syndrome Populations by Using Self-Normalizing Neural Networks and Feature Selection.GENES,9(4),208. |
| MLA | Pan, Xiaoyong,et al."Identifying Patients with Atrioventricular Septal Defect in Down Syndrome Populations by Using Self-Normalizing Neural Networks and Feature Selection".GENES 9.4(2018):208. |
入库方式： OAI收割
来源：上海营养与健康研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。