ArticleFrontiers in psychiatry2020
Prediction of Smoking Behavior From Single Nucleotide Polymorphisms With Machine Learning Approaches.
Article in Frontiers in psychiatry, 2020. 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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Who cites it
9 citing papers in PubMed.
- A Machine Learning Approach to Predicting Household Smoke Exposure Risk in Somalia: An Analysis With SHAP Explanations.Environmental health insights · 2026Article
- Harnessing machine learning in contemporary tobacco research.Toxicology reports · 2025Review
- Debiased lasso after sample splitting for estimation and inference in high-dimensional generalized linear models.The Canadian journal of statistics = Revue canadienne de statistique · 2025Article
- Nicotine Motivated Behavior inInternational journal of molecular sciences · 2024Article
- Smoking Classification Using Novel Plasma Cytokines by implementing Machine Learning and Statistical Methods.Proceedings. International Conference on Computational Science and Computational Intelligence · 2023Article
- Identification of a Novel Functional Non-synonymous Single Nucleotide Polymorphism in Frizzled Class Receptor 6 Gene for Involvement in Depressive Symptoms.Frontiers in molecular neuroscience · 2022Article
- Prediction of prokaryotic transposases from protein features with machine learning approaches.Microbial genomics · 2021Article
- Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles.PeerJ. Computer science · 2021Article
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
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. The SVM model with 500 top single nucleotide polymorphisms (SNPs) selected using logistic regression (p<0.01) as the feature selection method achieved an area under the curve (AUC) of 0.691, 0.721, and 0.720 for the training, test, and independent test samples, respectively. The RF model with 500 top SNPs selected using logistic regression (p<0.01) achieved AUCs of 0.671, 0.665, and 0.667 for the training, test, and independent test samples, respectively. Finally, we used the combined logistic (p<0.01) and LASSO (λ=10
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