Evaluation of penalized and machine learning methods for asthma disease prediction in the Korean Genome and Epidemiology Study (KoGES)
Abstract Background Genome-wide association studies have successfully identified genetic variants associated with human disease. Various statistical approaches based on penalized and machine learning methods have recently been proposed for disease prediction. In this study, we evaluated the performa...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2024-02-01
|
| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-024-05677-x |
| _version_ | 1797273062234652672 |
|---|---|
| author | Yongjun Choi Junho Cha Sungkyoung Choi |
| author_facet | Yongjun Choi Junho Cha Sungkyoung Choi |
| author_sort | Yongjun Choi |
| collection | DOAJ |
| description | Abstract Background Genome-wide association studies have successfully identified genetic variants associated with human disease. Various statistical approaches based on penalized and machine learning methods have recently been proposed for disease prediction. In this study, we evaluated the performance of several such methods for predicting asthma using the Korean Chip (KORV1.1) from the Korean Genome and Epidemiology Study (KoGES). Results First, single-nucleotide polymorphisms were selected via single-variant tests using logistic regression with the adjustment of several epidemiological factors. Next, we evaluated the following methods for disease prediction: ridge, least absolute shrinkage and selection operator, elastic net, smoothly clipped absolute deviation, support vector machine, random forest, boosting, bagging, naïve Bayes, and k-nearest neighbor. Finally, we compared their predictive performance based on the area under the curve of the receiver operating characteristic curves, precision, recall, F1-score, Cohen′s Kappa, balanced accuracy, error rate, Matthews correlation coefficient, and area under the precision-recall curve. Additionally, three oversampling algorithms are used to deal with imbalance problems. Conclusions Our results show that penalized methods exhibit better predictive performance for asthma than that achieved via machine learning methods. On the other hand, in the oversampling study, randomforest and boosting methods overall showed better prediction performance than penalized methods. |
| first_indexed | 2024-03-07T14:38:11Z |
| format | Article |
| id | doaj.art-cbfe60f1785c402bae0e11d76f409ba7 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-03-07T14:38:11Z |
| publishDate | 2024-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-cbfe60f1785c402bae0e11d76f409ba72024-03-05T20:31:47ZengBMCBMC Bioinformatics1471-21052024-02-0125112710.1186/s12859-024-05677-xEvaluation of penalized and machine learning methods for asthma disease prediction in the Korean Genome and Epidemiology Study (KoGES)Yongjun Choi0Junho Cha1Sungkyoung Choi2Department of Applied Artificial Intelligence, College of Computing, Hanyang UniversityDepartment of Applied Artificial Intelligence, College of Computing, Hanyang UniversityDepartment of Applied Artificial Intelligence, College of Computing, Hanyang UniversityAbstract Background Genome-wide association studies have successfully identified genetic variants associated with human disease. Various statistical approaches based on penalized and machine learning methods have recently been proposed for disease prediction. In this study, we evaluated the performance of several such methods for predicting asthma using the Korean Chip (KORV1.1) from the Korean Genome and Epidemiology Study (KoGES). Results First, single-nucleotide polymorphisms were selected via single-variant tests using logistic regression with the adjustment of several epidemiological factors. Next, we evaluated the following methods for disease prediction: ridge, least absolute shrinkage and selection operator, elastic net, smoothly clipped absolute deviation, support vector machine, random forest, boosting, bagging, naïve Bayes, and k-nearest neighbor. Finally, we compared their predictive performance based on the area under the curve of the receiver operating characteristic curves, precision, recall, F1-score, Cohen′s Kappa, balanced accuracy, error rate, Matthews correlation coefficient, and area under the precision-recall curve. Additionally, three oversampling algorithms are used to deal with imbalance problems. Conclusions Our results show that penalized methods exhibit better predictive performance for asthma than that achieved via machine learning methods. On the other hand, in the oversampling study, randomforest and boosting methods overall showed better prediction performance than penalized methods.https://doi.org/10.1186/s12859-024-05677-xDisease risk prediction modelLarge-scale genetic dataAsthmaPenalized methodsMachine learning methodsEnsemble methods |
| spellingShingle | Yongjun Choi Junho Cha Sungkyoung Choi Evaluation of penalized and machine learning methods for asthma disease prediction in the Korean Genome and Epidemiology Study (KoGES) BMC Bioinformatics Disease risk prediction model Large-scale genetic data Asthma Penalized methods Machine learning methods Ensemble methods |
| title | Evaluation of penalized and machine learning methods for asthma disease prediction in the Korean Genome and Epidemiology Study (KoGES) |
| title_full | Evaluation of penalized and machine learning methods for asthma disease prediction in the Korean Genome and Epidemiology Study (KoGES) |
| title_fullStr | Evaluation of penalized and machine learning methods for asthma disease prediction in the Korean Genome and Epidemiology Study (KoGES) |
| title_full_unstemmed | Evaluation of penalized and machine learning methods for asthma disease prediction in the Korean Genome and Epidemiology Study (KoGES) |
| title_short | Evaluation of penalized and machine learning methods for asthma disease prediction in the Korean Genome and Epidemiology Study (KoGES) |
| title_sort | evaluation of penalized and machine learning methods for asthma disease prediction in the korean genome and epidemiology study koges |
| topic | Disease risk prediction model Large-scale genetic data Asthma Penalized methods Machine learning methods Ensemble methods |
| url | https://doi.org/10.1186/s12859-024-05677-x |
| work_keys_str_mv | AT yongjunchoi evaluationofpenalizedandmachinelearningmethodsforasthmadiseasepredictioninthekoreangenomeandepidemiologystudykoges AT junhocha evaluationofpenalizedandmachinelearningmethodsforasthmadiseasepredictioninthekoreangenomeandepidemiologystudykoges AT sungkyoungchoi evaluationofpenalizedandmachinelearningmethodsforasthmadiseasepredictioninthekoreangenomeandepidemiologystudykoges |