ArticleInternational journal of molecular sciences2023
Genetic Risk Assessment of Nonsyndromic Cleft Lip with or without Cleft Palate by Linking Genetic Networks and Deep Learning Models.
Article in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- A Novel Nonsense Variant in TBX15 Transcription Factor Suggests an Expanded Genetic Spectrum Of Submucous Cleft Palate.International dental journal · 2026Article
- Artificial intelligence applications in the diagnosis and management of cleft lip and palate: An updated review.Journal of dental sciences · 2026Review
- The Evolution of Gene Sequencing Technologies: Unveiling Genetic Architecture of Nonsyndromic Orofacial Clefts.Genetics research · 2026Review
- Revolutionizing cleft lip and palate management through artificial intelligence: a scoping review.Oral and maxillofacial surgery · 2025Article
- Artificial Intelligence Applications in Pediatric Craniofacial Surgery.Diagnostics (Basel, Switzerland) · 2025Review
- Functional Validation of Noncoding Variants Associated With Nonsyndromic Orofacial Cleft.Human mutation · 2025Review
- Preliminary study of environmental risk and protective factors during pregnancy for cleft lip with or without palate in the Korean population.Korean journal of orthodontics · 2024Article
- Management of orofacial clefts in times of artificial intelligence: advances and challenges.European archives of paediatric dentistry : official journal of the European Academy of Paediatric Dentistry · 2024Article
- Artificial intelligence for maxillofacial prosthodontics: A technological shift in craniofacial rehabilitation- a scoping review.Journal of oral biology and craniofacial researchReview
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
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-control 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-protein interaction (PPI) network analyses. The
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Registered trials
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