Machine learning approach to single nucleotide polymorphism-based asthma prediction

• Published in: PloS one Vol. 14; no. 12; p. e0225574
• Main Authors: Gaudillo, Joverlyn, Rodriguez, Jae Joseph Russell, Nazareno, Allen, Baltazar, Lei Rigi, Vilela, Julianne, Bulalacao, Rommel, Domingo, Mario, Albia, Jason
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 04.12.2019; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Artificial intelligence; Asthma; Asthma - genetics; Biology and Life Sciences; Biomarkers; Breast cancer; Classification; Computer and Information Sciences; Disease; Genetic polymorphisms; Genetics; Genomes; Genomics; Humans; Hypothesis testing; Immunology; Integration; Interdisciplinary aspects; Laboratories; Learning algorithms; Machine Learning; Medical research; Medicine and Health Sciences; Model accuracy; Models, Theoretical; Nucleotides; Physical Sciences; Physics; Polymorphism; Polymorphism, Single Nucleotide - genetics; Predictions; Proteomics; Research and Analysis Methods; ROC Curve; Single nucleotide polymorphisms; Single-nucleotide polymorphism; Studies; Support Vector Machine; Support vector machines; Toxicology; Philippines
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0225574

Machine learning (ML) is poised as a transformational approach uniquely positioned to discover the hidden biological interactions for better prediction and diagnosis of complex diseases. In this work, we integrated ML-based models for feature selection and classification to quantify the risk of individual susceptibility to asthma using single nucleotide polymorphism (SNP). Random forest (RF) and recursive feature elimination (RFE) algorithm were implemented to identify the SNPs with high implication to asthma. K-nearest neighbor (kNN) and support vector machine (SVM) algorithms were trained to classify the identified SNPs whether associated with non-asthmatic or asthmatic samples. Feature selection step showed that RF outperformed RFE and the feature importance score derived from RF was consistently high for a subset of SNPs, indicating the robustness of RF in selecting relevant features associated with asthma. Model comparison showed that the integration of RF-SVM obtained the highest model performance with an accuracy, precision, and sensitivity of 62.5%, 65.3%, and 69%, respectively, when compared to the baseline, RF-kNN, and an external MeanDiff-kNN models. Furthermore, results show that the occurrence of asthma can be predicted with an Area under the Curve (AUC) of 0.62 and 0.64 for RF-SVM and RF-kNN models, respectively. This study demonstrates the integration of ML models to augment traditional methods in predicting genetic predisposition to multifactorial diseases such as asthma.