Prediction of Smoking Behavior From Single Nucleotide Polymorphisms With Machine Learning Approaches.

ABSTRACT: Smoking is a complex behavior with a heritability as high as 50%. Given such a large genetic contribution, it provides an opportunity to prevent those individuals who are susceptible to smoking dependence from ever starting to smoke by predicting their inherited predisposition with their genomic profiles. Although previous studies have identified many susceptibility variants for smoking, they have limited power to predict smoking behavior. We applied the support vector machine (SVM) and random forest (RF) methods to build prediction models for smoking behavior. We first used 1,431 smokers and 1,503 non-smokers of African origin for model building with a 10-fold cross-validation and then tested the prediction models on an independent dataset consisting of 213 smokers and 224 non-smokers. Th

SUBMITTER: Xu Y 

PROVIDER: S-EPMC7241440 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature