ArticleGenes2018
Identifying Patients with Atrioventricular Septal Defect in Down Syndrome Populations by Using Self-Normalizing Neural Networks and Feature Selection.
Article in Genes, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
23 citing papers in PubMed, 34 citations in OpenAlex.
- Challenges in the Surgical Treatment of Atrioventricular Septal Defect in Children With and Without Down Syndrome in Romania-A Developing Country.Frontiers in pediatrics · 2021Article
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- The Key Genes for Perineural Invasion in Pancreatic Ductal Adenocarcinoma Identified With Monte-Carlo Feature Selection Method.Frontiers in genetics · 2020Article
- Integrative Analysis of Methylation and Gene Expression in Lung Adenocarcinoma and Squamous Cell Lung Carcinoma.Frontiers in bioengineering and biotechnology · 2020Article
- The Serum MicroRNA Signatures for Pancreatic Cancer Detection and Operability Evaluation.Frontiers in bioengineering and biotechnology · 2020Article
- Identification of Post-myocardial Infarction Blood Expression Signatures Using Multiple Feature Selection Strategies.Frontiers in physiology · 2020Article
- The Blood Gene Expression Signature for Kawasaki Disease in Children Identified with Advanced Feature Selection Methods.BioMed research international · 2020Article
- Investigation and Prediction of Human Interactome Based on Quantitative Features.Frontiers in bioengineering and biotechnology · 2020Article
- Pan-Cancer Classification Based on Self-Normalizing Neural Networks and Feature Selection.Frontiers in bioengineering and biotechnology · 2020Article
- Identification and Analysis of the Blood lncRNA Signature for Liver Cirrhosis and Hepatocellular Carcinoma.Frontiers in genetics · 2020Article
- Identification of Common Genes and Pathways in Eight Fibrosis Diseases.Frontiers in genetics · 2020Article
- Multi-Omics Analysis of Acute Lymphoblastic Leukemia Identified the Methylation and Expression Differences Between BCP-ALL and T-ALL.Frontiers in cell and developmental biology · 2020Article
- The Functional Effects of Key Driver KRAS Mutations on Gene Expression in Lung Cancer.Frontiers in genetics · 2020Article
- Identifying Methylation Pattern and Genes Associated with Breast Cancer Subtypes.International journal of molecular sciences · 2019Article
- HIV infection alters the human epigenetic landscape.Gene therapy · 2019Article
- Screening of Methylation Signature and Gene Functions Associated With the Subtypes of Isocitrate Dehydrogenase-Mutation Gliomas.Frontiers in bioengineering and biotechnology · 2019Article
- Primary Tumor Site Specificity is Preserved in Patient-Derived Tumor Xenograft Models.Frontiers in genetics · 2019Article
- Identifying Microbiota Signature and Functional Rules Associated With Bacterial Subtypes in Human Intestine.Frontiers in genetics · 2019Article
- Classification of Widely and Rarely Expressed Genes with Recurrent Neural Network.Computational and structural biotechnology journal · 2019Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.