Genetic Risk Assessment of Nonsyndromic Cleft Lip with or without Cleft Palate by Linking Genetic Networks and Deep Learning Models

多因子降维法 支持向量机 特征选择 人工智能 随机森林 机器学习 计算机科学 人工神经网络 深度学习 单核苷酸多态性 生物 遗传学 基因 基因型
作者
Geon Kang,Seung-Hak Baek,Young Ho Kim,Dong-Hyun Kim,Ji Wan Park
出处
期刊:International Journal of Molecular Sciences [MDPI AG]
卷期号:24 (5): 4557-4557
标识
DOI:10.3390/ijms24054557
摘要

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 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.
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