Machine learning approach to single nucleotide polymorphism-based asthma prediction

Autoři: Joverlyn Gaudillo aff001;  Jae Joseph Russell Rodriguez aff002;  Allen Nazareno aff001;  Lei Rigi Baltazar aff001;  Julianne Vilela aff003;  Rommel Bulalacao aff004;  Mario Domingo aff004;  Jason Albia aff001
Působiště autorů: Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños, Philippines aff001;  Genetics and Molecular Biology Division, Institute of Biological Sciences, University of the Philippines Los Baños, Philippines aff002;  Philippine Genome Center Program for Agriculture, Office of the Vice Chancellor for Research and Extension, University of the Philippines Los Baños, Philippines aff003;  Domingo Artificial Intelligence Research Center, Los Baños, Philippines aff004;  Computational Interdisciplinary Research Laboratories (CINTERLabs), University of the Philippines Los Baños, Philippines aff005
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
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.

Klíčová slova:

Asthma – Genetics of disease – Genome-wide association studies – Machine learning – Machine learning algorithms – Molecular genetics – Support vector machines


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2019 Číslo 12