{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T17:36:57Z","timestamp":1787161017641,"version":"build-2736575974"},"reference-count":89,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,4,12]],"date-time":"2018-04-12T00:00:00Z","timestamp":1523491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31371335"],"award-info":[{"award-number":["31371335"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31701151"],"award-info":[{"award-number":["31701151"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31571343"],"award-info":[{"award-number":["31571343"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007219","name":"Natural Science Foundation of Shanghai","doi-asserted-by":"publisher","award":["17ZR1412500"],"award-info":[{"award-number":["17ZR1412500"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007219","name":"Natural Science Foundation of Shanghai","doi-asserted-by":"publisher","award":["16ZR1403100"],"award-info":[{"award-number":["16ZR1403100"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Sailing Program"},{"DOI":"10.13039\/501100004739","name":"Youth Innovation Promotion Association of Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["2016245"],"award-info":[{"award-number":["2016245"]}],"id":[{"id":"10.13039\/501100004739","id-type":"DOI","asserted-by":"publisher"}]},{"name":"fund of the key Laboratory of Stem Cell Biology of Chinese Academy of Sciences","award":["201703"],"award-info":[{"award-number":["201703"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Genes"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/genes9040208","type":"journal-article","created":{"date-parts":[[2018,4,12]],"date-time":"2018-04-12T12:19:27Z","timestamp":1523535567000},"page":"208","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Identifying Patients with Atrioventricular Septal Defect in Down Syndrome Populations by Using Self-Normalizing Neural Networks and Feature Selection"],"prefix":"10.3390","volume":"9","author":[{"given":"Xiaoyong","family":"Pan","sequence":"first","affiliation":[{"name":"College of Life Science, Shanghai University, Shanghai 200444, China"},{"name":"Department of Medical Informatics, Erasmus MC, 3015 CE Rotterdam, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohua","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Computational Biology, School of Life Sciences, Fudan University, Shanghai 200438, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaiyan","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Guangdong AIB Polytechnic, Guangzhou 510507, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shao","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Life Science, Shanghai University, Shanghai 200444, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3068-1583","authenticated-orcid":false,"given":"Lei","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1975-9693","authenticated-orcid":false,"given":"Tao","family":"Huang","sequence":"additional","affiliation":[{"name":"Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5664-7979","authenticated-orcid":false,"given":"Yu","family":"Cai","sequence":"additional","affiliation":[{"name":"College of Life Science, Shanghai University, Shanghai 200444, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1111\/chd.12235","article-title":"Public health research in congenital heart disease","volume":"9","author":"Lara","year":"2014","journal-title":"Congenit. Heart Dis."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1161\/CIR.0000000000000353","article-title":"Transplantation and mechanical circulatory support in congenital heart disease: A scientific statement from the American heart association","volume":"133","author":"Ross","year":"2016","journal-title":"Circulation"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2372","DOI":"10.1016\/j.jacc.2015.09.042","article-title":"Eligibility and disqualification recommendations for competitive athletes with cardiovascular abnormalities: Task force 4: Congenital heart disease: A scientific statement from the American heart association and American college of cardiology","volume":"66","author":"Ackerman","year":"2015","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1590\/1516-3180.2015.00710108","article-title":"Down syndrome: Prevalence and distribution of congenital heart disease in brazil","volume":"133","author":"Bermudez","year":"2015","journal-title":"Sao Paulo Med. J."},{"key":"ref_5","first-page":"267","article-title":"Cardiac biomarkers of low cardiac output syndrome in the postoperative period after congenital heart disease surgery in children","volume":"70","year":"2017","journal-title":"Rev. Esp. Cardiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"S10","DOI":"10.1016\/j.jvc.2015.04.005","article-title":"Sequential segmental classification of feline congenital heart disease","volume":"17","author":"Scansen","year":"2015","journal-title":"J. Vet. Cardiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1161\/CIRCIMAGING.112.000189","article-title":"Echocardiographic features defining right dominant unbalanced atrioventricular septal defect: A multi-institutional congenital heart surgeons\u2019 society study","volume":"6","author":"Cohen","year":"2013","journal-title":"Circ. Cardiovasc. Imaging"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1552","DOI":"10.1136\/heartjnl-2016-309437","article-title":"Eisenmenger syndrome and long-term survival in patients with down syndrome and congenital heart disease","volume":"102","author":"Korten","year":"2016","journal-title":"Heart"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1590\/0103-0582201432218913","article-title":"Prevalence and profile of congenital heart disease and pulmonary hypertension in down syndrome in a pediatric cardiology service","volume":"32","author":"Mourato","year":"2014","journal-title":"Rev. Paul. Pediatr."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"357","DOI":"10.4070\/kcj.2015.45.5.357","article-title":"Genetic syndromes associated with congenital heart disease","volume":"45","author":"Ko","year":"2015","journal-title":"Korean Circ. J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1097\/00001573-200105000-00006","article-title":"Genetic basis of syndromes associated with congenital heart disease","volume":"16","author":"Gelb","year":"2001","journal-title":"Curr. Opin. Cardiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2843","DOI":"10.1002\/ajmg.a.35626","article-title":"Polymorphic haplotypes of CRELD1 differentially predispose Down syndrome and euploids individuals to atrioventricular septal defect","volume":"158A","author":"Ghosh","year":"2012","journal-title":"Am. J. Med. Genet. A"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1007\/s00246-014-1091-3","article-title":"Germline mutations in NKX2-5, GATA4, and CRELD1 are rare in a Mexican sample of down syndrome patients with endocardial cushion and septal heart defects","volume":"36","year":"2015","journal-title":"Pediatr. Cardiol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"458","DOI":"10.14503\/THIJ-14-4256","article-title":"Down syndrome with complete atrioventricular septal defect, hypertrophic cardiomyopathy, and pulmonary vein stenosis","volume":"42","author":"Mahadevaiah","year":"2015","journal-title":"Tex. Heart Inst. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1536\/ihj.15-319","article-title":"Bioinformatic analysis of genes and microRNAs associated with atrioventricular septal defect in down syndrome patients","volume":"57","author":"Wang","year":"2016","journal-title":"Int. Heart J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1534\/g3.117.300366","article-title":"Analysis of copy number variants on chromosome 21 in down syndrome-associated congenital heart defects","volume":"8","author":"Rambomartin","year":"2018","journal-title":"G3"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/978-1-4939-3578-9_5","article-title":"The gene expression omnibus database","volume":"1418","author":"Clough","year":"2016","journal-title":"Methods Mol. Biol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ostrovnaya, I., Nanjangud, G., and Olshen, A.B. (2010). A classification model for distinguishing copy number variants from cancer-related alterations. BMC Bioinform., 11.","DOI":"10.1186\/1471-2105-11-297"},{"key":"ref_19","first-page":"1","article-title":"Application of machine learning to development of copy number variation-based prediction of cancer risk","volume":"7","author":"Ding","year":"2014","journal-title":"Genom. Insights"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"878","DOI":"10.15252\/msb.20156651","article-title":"Deep learning for computational biology","volume":"12","author":"Angermueller","year":"2016","journal-title":"Mol. Syst. Biol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_22","first-page":"1Y3V4C","article-title":"An extensive survey on deep learning applications","volume":"6","author":"Mohan","year":"2017","journal-title":"IJIACS"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-014-0007-7","article-title":"Deep learning applications and challenges in big data analytics","volume":"2","author":"Najafabadi","year":"2015","journal-title":"J. Big Data"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Pan, X.Y., and Shen, H.B. (2017). RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach. BMC Bioinform., 18.","DOI":"10.1186\/s12859-017-1561-8"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Pan, X.Y., Fan, Y.X., Yan, J.C., and Shen, H.B. (2016). IPMiner: Hidden ncRNA-protein interaction sequential pattern mining with stacked autoencoder for accurate computational prediction. BMC Genom., 17.","DOI":"10.1186\/s12864-016-2931-8"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhang, C., Yan, J., Li, C., Rui, X., Liu, L., and Bie, F. (2016, January 15\u201319). On Estimating Air Pollution from Photos Using Convolutional Neural Network. Proceedings of the 2016 ACM on Multimedia Conference, Amsterdam, The Netherlands.","DOI":"10.1145\/2964284.2967230"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ren, Z., Yan, J., Ni, B., Zha, H., and Yang, X. (2017, January 4\u20139). Unsupervised Deep Learning for Optical Flow Estimation. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.10723"},{"key":"ref_28","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/72.279181","article-title":"Learning long-term dependencies with gradient descent is difficult","volume":"5","author":"Bengio","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_30","unstructured":"Pascanu, R., Mikolov, T., and Bengio, Y. Understanding the exploding gradient problem, CoRR, abs\/1211.5063 2012."},{"key":"ref_31","unstructured":"Pascanu, R., Mikolov, T., and Bengio, Y. (2013, January 16\u201321). On the Difficulty of Training Recurrent Neural Networks. Proceedings of the International Conference on Machine Learning, Atlanta, GA, USA."},{"key":"ref_32","unstructured":"Klambauer, G., Unterthiner, T., Mayr, A., and Hochreiter, S. (2017). Self-normalizing neural networks. Adv. Neural Inf. Process. Syst., 972\u2013981."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_35","unstructured":"Rambo-Martin, B.L., and Zwick, M.E. (2017, January 30). Analysis of Copy Number Variants on Chromosome 21 in Down Syndrome-Associated Congenital Heart Defects, Available online: https:\/\/www.ncbi.nlm.nih.gov\/geo\/query\/acc.cgi?acc=GSE93004."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1093\/bioinformatics\/btm486","article-title":"Monte carlo feature selection for supervised classification","volume":"24","author":"Draminski","year":"2008","journal-title":"Bioinformatics"},{"key":"ref_37","unstructured":"Draminski, M. (2018, April 05). Available online:. Available online: http:\/\/www.ipipan.eu\/staff\/m.draminski\/mcfs.html."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/S0022-0000(74)80044-9","article-title":"Approximation algorithms for combinatorial problems","volume":"9","author":"Johnson","year":"1974","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_39","unstructured":"Cohen, W.W. (1995, January 9\u201312). Fast Effective Rule Induction. Proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, CA, USA."},{"key":"ref_40","unstructured":"Johannes, F., and Widmer, G. (1994, January 10\u201313). Incremental Reduced Error Pruning. Proceedings of the Eleventh Annual Conference on Machine Learning, New Brunswick, NJ, USA."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/BF00117105","article-title":"Learning logical definitions from relations","volume":"266","author":"Quinlan","year":"1990","journal-title":"Mach. Learn."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Brunnk, C.A., and Pazzani, M.J. (1991). An investigation of noise-tolerant relational concept learning algorithms. Proceedings of the 8th International Workshop on Machine Learning, Evanston, IL, USA, 1991, Elsevier.","DOI":"10.1016\/B978-1-55860-200-7.50080-5"},{"key":"ref_43","unstructured":"Ho, T.K. (1995, January 14\u201316). Random Decision Forests. Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, USA."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","article-title":"The random subspace method for constructing decision forests","volume":"20","author":"Ho","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_45","unstructured":"Ting, K.M., and Witten, I.H. (1997, January 8\u201312). Stacking Bagged and Dagged Models. Proceedings of the Fourteenth International Conference on Machine Learning, San Francisco, CA, USA."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2065","DOI":"10.1007\/s00438-016-1240-x","article-title":"Identification of compound\u2013protein interactions through the analysis of gene ontology, KEGG enrichment for proteins and molecular fragments of compounds","volume":"291","author":"Chen","year":"2016","journal-title":"Mol. Genet. Genom."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.1007\/s00726-015-1974-5","article-title":"Prediction and analysis of cell-penetrating peptides using pseudo-amino acid composition and random forest models","volume":"47","author":"Chen","year":"2015","journal-title":"Amino Acids"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"22","DOI":"10.2174\/1574893611666151119215131","article-title":"Prediction of linear B-cell epitopes with mRMR feature selection and analysis","volume":"11","author":"Li","year":"2016","journal-title":"Curr. Bioinform."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Li, B.Q., Hu, L.L., Chen, L., Feng, K.Y., Cai, Y.D., and Chou, K.C. (2012). Prediction of protein domain with mRMR feature selection and analysis. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0039308"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Casanova, R., Saldana, S., Chew, E.Y., Danis, R.P., Greven, C.M., and Ambrosius, W.T. (2014). Application of random forests methods to diabetic retinopathy classification analyses. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0098587"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.jtbi.2010.10.037","article-title":"AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties","volume":"270","author":"Kandaswamy","year":"2011","journal-title":"J. Theor. Biol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"50","DOI":"10.2174\/092986612798472875","article-title":"RSARF: Prediction of residue solvent accessibility from protein sequence using random forest method","volume":"19","author":"Pugalenthi","year":"2011","journal-title":"Protein Pept. Lett."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"4992","DOI":"10.1021\/pr100618t","article-title":"Large-scale prediction of human protein-protein interactions from amino acid sequence based on latent topic features","volume":"9","author":"Pan","year":"2010","journal-title":"J. Proteome Res."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"164","DOI":"10.2174\/1386207319666161227124350","article-title":"Predicting citrullination sites in protein sequences using mRMR method and random forest algorithm","volume":"20","author":"Zhang","year":"2017","journal-title":"Comb. Chem. High Throughput Screen"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"23075","DOI":"10.1038\/srep23075","article-title":"Gene expression profiling gut microbiota in different races of humans","volume":"6","author":"Chen","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_56","unstructured":"Witten, I.H., and Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann."},{"key":"ref_57","unstructured":"Kohavi, R. (1995, January 20\u201325). A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montreal, QC, Canada."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"26582","DOI":"10.1109\/ACCESS.2017.2775703","article-title":"Identify key sequence features to improve CRISPR sgRNA efficacy","volume":"5","author":"Chen","year":"2017","journal-title":"IEEE Access"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3394","DOI":"10.1002\/jcb.26507","article-title":"Identification of gene expression signatures across different types of neural stem cells with the Monte-carlo feature selection method","volume":"119","author":"Chen","year":"2017","journal-title":"J. Cell. Biochem."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.artmed.2017.02.001","article-title":"Analysis of cancer-related lncRNAs using gene ontology and KEGG pathways","volume":"76","author":"Chen","year":"2017","journal-title":"Artif. Intell. Med."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Chen, L., Zeng, W.-M., Cai, Y.-D., Feng, K.-Y., and Chou, K.-C. (2012). Predicting anatomical therapeutic chemical (ATC) classification of drugs by integrating chemical-chemical interactions and similarities. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0035254"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"526","DOI":"10.2174\/1574893611666160618094219","article-title":"Identification of drug-drug interactions using chemical interactions","volume":"12","author":"Chen","year":"2017","journal-title":"Curr. Bioinform."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Chen, L., Feng, K.Y., Cai, Y.D., Chou, K.C., and Li, H.P. (2010). Predicting the network of substrate-enzyme-product triads by combining compound similarity and functional domain composition. BMC Bioinform., 11.","DOI":"10.1186\/1471-2105-11-293"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/0005-2795(75)90109-9","article-title":"Comparison of the predicted and observed secondary structure of T4 phage lysozyme","volume":"405","author":"Matthews","year":"1975","journal-title":"Biochim. Biophys. Acta"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1038\/gim.2014.144","article-title":"Contribution of copy-number variation to down syndrome-associated atrioventricular septal defects","volume":"17","author":"Ramachandran","year":"2015","journal-title":"Genet. Med."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1006\/geno.2000.6258","article-title":"Isolation and characterization of a human chromosome 21q22.3 gene (WDR4) and its mouse homologue that code for a WD-repeat protein","volume":"68","author":"Michaud","year":"2000","journal-title":"Genomics"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Lim, J.H., Kim, S.Y., Park, S.Y., Lee, S.Y., Kim, M.J., Han, Y.J., Lee, S.W., Chung, J.H., Kim, M.Y., and Yang, J.H. (2011). Non-invasive epigenetic detection of fetal trisomy 21 in first trimester maternal plasma. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0027709"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"836","DOI":"10.1111\/j.1365-2141.2008.07264.x","article-title":"High expression of the cGMP-specific phosphodiesterase, PDE9a, in sickle cell disease (SCD) and the effects of its inhibition in erythroid cells and SCD neutrophils","volume":"142","author":"Almeida","year":"2008","journal-title":"Br. J. Haematol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1038\/nature14332","article-title":"Phosphodiesterase 9A controls nitric-oxide-independent cGMP and hypertrophic heart disease","volume":"519","author":"Lee","year":"2015","journal-title":"Nature"},{"key":"ref_70","first-page":"441","article-title":"Metabolites of the L-arginine-NO pathway in patients with left-to-right shunt","volume":"47","author":"Gorenflo","year":"2001","journal-title":"Clin. Lab."},{"key":"ref_71","first-page":"1184","article-title":"Plasma L-arginine and metabolites of nitric oxide synthase in patients with left-to-right shunt after intracardiac repair","volume":"127","author":"Gorenflo","year":"2005","journal-title":"Chest"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1097\/00003246-199706000-00026","article-title":"Endogenous nitric oxide production and atrial natriuretic peptide biological activity in infants undergoing cardiac operations","volume":"25","author":"Seghaye","year":"1997","journal-title":"Crit. Care Med."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1093\/dnares\/dsi004","article-title":"C21orf5, a new member of dopey family involved in morphogenesis, could participate in neurological alterations and mental retardation in down syndrome","volume":"12","author":"Rachidi","year":"2005","journal-title":"DNA Res."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1159\/000087509","article-title":"C21orf5, a human candidate gene for brain abnormalities and mental retardation in down syndrome","volume":"112","author":"Rachidi","year":"2006","journal-title":"Cytogenet. Genome Res."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1038\/nrg.2016.154","article-title":"Down syndrome and the complexity of genome dosage imbalance","volume":"18","author":"Antonarakis","year":"2017","journal-title":"Nat. Rev. Genet."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1007\/s00018-017-2629-3","article-title":"Mechanisms of ciliary targeting: Entering importins and rabs","volume":"75","author":"Lu","year":"2018","journal-title":"Cell. Mol. Life Sci."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1787","DOI":"10.1007\/s00018-016-2148-7","article-title":"Mechanism of ciliary disassembly","volume":"73","author":"Liang","year":"2016","journal-title":"Cell. Mol. Life Sci."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1046\/j.1442-2042.1999.06441.x","article-title":"Analysis of prognostic factors related to primary superficial bladder cancer tumor recurrence in prophylactic intravesical epirubicin therapy","volume":"6","author":"Kondo","year":"1999","journal-title":"Int. J. Urol."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ydbio.2007.04.043","article-title":"Calcium signaling in vertebrate embryonic patterning and morphogenesis","volume":"307","author":"Slusarski","year":"2007","journal-title":"Dev. Biol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1146\/annurev.genom.9.081307.164217","article-title":"Copy number variation in human health, disease, and evolution","volume":"10","author":"Zhang","year":"2009","journal-title":"Annu. Rev. Genom. Hum. Genet."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1016\/j.ajhg.2015.03.007","article-title":"Copy-number variation of the glucose transporter gene SLC2A3 and congenital heart defects in the 22q11.2 deletion syndrome","volume":"96","author":"Mlynarski","year":"2015","journal-title":"Am. J. Hum. Genet."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.bbagrm.2007.09.005","article-title":"Analysis of the promoter region of human placenta-specific DSCR4 gene","volume":"1779","author":"Asai","year":"2008","journal-title":"Biochim. Biophys. Acta"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"36","DOI":"10.5409\/wjcp.v2.i4.36","article-title":"Echocardiography in children with down syndrome","volume":"2","year":"2013","journal-title":"World J. Clin. Pediatr."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.ygeno.2017.06.004","article-title":"Genotype-phenotype correlation for congenital heart disease in down syndrome through analysis of partial trisomy 21 cases","volume":"109","author":"Pelleri","year":"2017","journal-title":"Genomics"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1086\/520000","article-title":"Classification of human chromosome 21 gene-expression variations in down syndrome: Impact on disease phenotypes","volume":"81","author":"Aubert","year":"2007","journal-title":"Am. J. Hum. Genet."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.semnephrol.2010.01.007","article-title":"Genetic factors predisposing to systemic lupus erythematosus and lupus nephritis","volume":"30","author":"Ramos","year":"2010","journal-title":"Semin. Nephrol."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.nbd.2010.11.010","article-title":"Genome-wide expression studies in autism spectrum disorder, rett syndrome, and down syndrome","volume":"45","author":"Lintas","year":"2012","journal-title":"Neurobiol. Dis."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1007\/s00439-011-0980-2","article-title":"Genetic analysis of down syndrome-associated heart defects in mice","volume":"130","author":"Liu","year":"2011","journal-title":"Hum. Genet."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"e26","DOI":"10.1136\/jmg.2003.011833","article-title":"Tetrasomy 21pter\u2192q21.2 in a male infant without typical down\u2019s syndrome dysmorphic features but moderate mental retardation","volume":"41","author":"Rost","year":"2004","journal-title":"J. Med. Genet."}],"container-title":["Genes"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-4425\/9\/4\/208\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:00:27Z","timestamp":1760194827000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-4425\/9\/4\/208"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,12]]},"references-count":89,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2018,4]]}},"alternative-id":["genes9040208"],"URL":"https:\/\/doi.org\/10.3390\/genes9040208","relation":{},"ISSN":["2073-4425"],"issn-type":[{"value":"2073-4425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,4,12]]}}}