Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 최성경 | - |
dc.date.accessioned | 2024-03-05T06:29:43Z | - |
dc.date.available | 2024-03-05T06:29:43Z | - |
dc.date.issued | 2024-02-02 | - |
dc.identifier.citation | BMC BIOINFORMATICS | en_US |
dc.identifier.issn | 1471-2105 | en_US |
dc.identifier.uri | https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-024-05677-x | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/189485 | - |
dc.description.abstract | BackgroundGenome-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).ResultsFirst, 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, naive 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.ConclusionsOur 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. | en_US |
dc.description.sponsorship | This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No.2018R1C1B6008277 and 2022R1F1A1072274). This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.RS-2022–00155885, Artifcial Intelligence Convergence Innovation Human Resources Development (Hanyang University ERICA)). This research was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (2019M3E5D3073365). This study was conducted using bioresources from National Biobank of Korea, the Korea Disease Control and Prevention Agency, Republic of Korea (KBN-2020-106). | en_US |
dc.language | en_US | en_US |
dc.publisher | BMC | en_US |
dc.relation.ispartofseries | v. 25, Article number: 56;1-27 | - |
dc.subject | NAIVE Bayes classification | en_US |
dc.subject | MACHINE learning | en_US |
dc.subject | RECEIVER operating characteristic curves | en_US |
dc.subject | GENOME-wide association studies | en_US |
dc.subject | EPIDEMIOLOGY | en_US |
dc.subject | K-nearest neighbor classification | en_US |
dc.subject | GENOMES | en_US |
dc.subject | SUPPORT vector machines | en_US |
dc.subject | Asthma | en_US |
dc.subject | Disease risk prediction model | en_US |
dc.subject | Ensemble methods | en_US |
dc.subject | Genome-wide association study | en_US |
dc.subject | GWAS | en_US |
dc.subject | KoGES | en_US |
dc.subject | Korean Genome and Epidemiology Study | en_US |
dc.subject | Large-scale genetic data | en_US |
dc.subject | Machine learning methods | en_US |
dc.subject | Oversampling | en_US |
dc.subject | Penalized methods | en_US |
dc.title | Evaluation of penalized and machine learning methods for asthma disease prediction in the Korean Genome and Epidemiology Study (KoGES) | en_US |
dc.type | Article | en_US |
dc.relation.no | 1 | - |
dc.relation.volume | 25 | - |
dc.identifier.doi | 10.1186/s12859-024-05677-x | en_US |
dc.relation.page | 1-27 | - |
dc.relation.journal | BMC BIOINFORMATICS | - |
dc.contributor.googleauthor | Choi, Yongjun | - |
dc.contributor.googleauthor | Cha, Junho | - |
dc.contributor.googleauthor | Choi, Sungkyoung | - |
dc.relation.code | 2024007632 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF SCIENCE AND CONVERGENCE TECHNOLOGY[E] | - |
dc.sector.department | DEPARTMENT OF MATHEMATICAL DATA SCIENCE | - |
dc.identifier.pid | day0413 | - |
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