Next Article in Journal
A Novel G-Protein-Coupled Receptors Gene from Upland Cotton Enhances Salt Stress Tolerance in Transgenic Arabidopsis
Next Article in Special Issue
Weighted Gene Co-Expression Network Analysis Reveals Dysregulation of Mitochondrial Oxidative Phosphorylation in Eating Disorders
Previous Article in Journal
Gene Therapy for Chronic HBV—Can We Eliminate cccDNA?
Previous Article in Special Issue
Identification of Key Pathways and Genes in the Dynamic Progression of HCC Based on WGCNA
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Genes 2018, 9(4), 208; https://doi.org/10.3390/genes9040208

Identifying Patients with Atrioventricular Septal Defect in Down Syndrome Populations by Using Self-Normalizing Neural Networks and Feature Selection

1
College of Life Science, Shanghai University, Shanghai 200444, China
2
Department of Medical Informatics, Erasmus MC, 3015 CE Rotterdam, The Netherlands
3
Department of Biostatistics and Computational Biology, School of Life Sciences, Fudan University, Shanghai 200438, China
4
Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
5
Department of Computer Science, Guangdong AIB Polytechnic, Guangzhou 510507, China
6
College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Received: 15 March 2018 / Revised: 28 March 2018 / Accepted: 3 April 2018 / Published: 12 April 2018
(This article belongs to the Special Issue Computational Approaches for Disease Gene Identification)
Full-Text   |   PDF [7271 KB, uploaded 3 May 2018]   |  

Abstract

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. View Full-Text
Keywords: atrioventricular septal defect; Down syndrome; self-normalizing neural network; Monte Carlo feature selection; random forest atrioventricular septal defect; Down syndrome; self-normalizing neural network; Monte Carlo feature selection; random forest
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Pan, X.; Hu, X.; Zhang, Y.H.; Feng, K.; Wang, S.P.; Chen, L.; Huang, T.; Cai, Y.D. Identifying Patients with Atrioventricular Septal Defect in Down Syndrome Populations by Using Self-Normalizing Neural Networks and Feature Selection. Genes 2018, 9, 208.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Genes EISSN 2073-4425 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top