{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T20:56:02Z","timestamp":1791665762013,"version":"4.3.4"},"reference-count":47,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,25]],"date-time":"2023-02-25T00:00:00Z","timestamp":1677283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"Korea government (MSIT)","doi-asserted-by":"publisher","award":["2021R1F1A1059803"],"award-info":[{"award-number":["2021R1F1A1059803"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"Korea government (MSIT)","doi-asserted-by":"publisher","award":["HRF-201711-015"],"award-info":[{"award-number":["HRF-201711-015"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hallym University Research Fund","award":["2021R1F1A1059803"],"award-info":[{"award-number":["2021R1F1A1059803"]}]},{"name":"Hallym University Research Fund","award":["HRF-201711-015"],"award-info":[{"award-number":["HRF-201711-015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Recent deep learning algorithms have further improved risk classification capabilities. However, an appropriate feature selection method is required to overcome dimensionality issues in population-based genetic studies. In this Korean case\u2013control study of nonsyndromic cleft lip with or without cleft palate (NSCL\/P), we compared the predictive performance of models that were developed by using the genetic-algorithm-optimized neural networks ensemble (GANNE) technique with those models that were generated by eight conventional risk classification methods, including polygenic risk score (PRS), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and deep-learning-based artificial neural network (ANN). GANNE, which is capable of automatic input SNP selection, exhibited the highest predictive power, especially in the 10-SNP model (AUC of 88.2%), thus improving the AUC by 23% and 17% compared to PRS and ANN, respectively. Genes mapped with input SNPs that were selected by using a genetic algorithm (GA) were functionally validated for risks of developing NSCL\/P in gene ontology and protein\u2013protein interaction (PPI) network analyses. The IRF6 gene, which is most frequently selected via GA, was also a major hub gene in the PPI network. Genes such as RUNX2, MTHFR, PVRL1, TGFB3, and TBX22 significantly contributed to predicting NSCL\/P risk. GANNE is an efficient disease risk classification method using a minimum optimal set of SNPs; however, further validation studies are needed to ensure the clinical utility of the model for predicting NSCL\/P risk.<\/jats:p>","DOI":"10.3390\/ijms24054557","type":"journal-article","created":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T03:29:48Z","timestamp":1677468588000},"page":"4557","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Genetic Risk Assessment of Nonsyndromic Cleft Lip with or without Cleft Palate by Linking Genetic Networks and Deep Learning Models"],"prefix":"10.3390","volume":"24","author":[{"given":"Geon","family":"Kang","sequence":"first","affiliation":[{"name":"Department of Medical Genetics, College of Medicine, Hallym University, Chuncheon 24252, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seung-Hak","family":"Baek","sequence":"additional","affiliation":[{"name":"Department of Orthodontics, School of Dentistry, Seoul National University, Seoul 03080, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Young Ho","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Orthodontics, The Institute of Oral Health Science, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul 06351, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1492-5253","authenticated-orcid":false,"given":"Dong-Hyun","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Social and Preventive Medicine, College of Medicine, Hallym University, Chuncheon 24252, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0840-1696","authenticated-orcid":false,"given":"Ji Wan","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Medical Genetics, College of Medicine, Hallym University, Chuncheon 24252, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1038\/nrg2933","article-title":"Cleft lip and palate: Understanding genetic and environmental influences","volume":"12","author":"Dixon","year":"2011","journal-title":"Nat. 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