Machine Learning Approaches for Predicting Bisphosphonate-Related Osteonecrosis in Women with Osteoporosis Using VEGFA Gene Polymorphisms
DC Field | Value | Language |
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dc.contributor.author | Kim, Jin-Woo | - |
dc.contributor.author | Yee, Jeong | - |
dc.contributor.author | Oh, Sang-Hyeon | - |
dc.contributor.author | Kim, Sun-Hyun | - |
dc.contributor.author | Kim, Sun-Jong | - |
dc.contributor.author | Chung, Jee-Eun | - |
dc.contributor.author | Gwak, Hye-Sun | - |
dc.date.accessioned | 2021-07-28T08:08:20Z | - |
dc.date.available | 2021-07-28T08:08:20Z | - |
dc.date.created | 2021-07-22 | - |
dc.date.issued | 2021-06 | - |
dc.identifier.issn | 2075-4426 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/105737 | - |
dc.description.abstract | Objective: This nested case-control study aimed to investigate the effects of VEGFA polymorphisms on the development of bisphosphonate-related osteonecrosis of the jaw (BRONJ) in women with osteoporosis. Methods: Eleven single nucleotide polymorphisms (SNPs) of the VEGFA were assessed in a total of 125 patients. Logistic regression was performed for multivariable analysis. Machine learning algorithms, namely, fivefold cross-validated multivariate logistic regression, elastic net, random forest, and support vector machine, were developed to predict risk factors for BRONJ occurrence. Area under the receiver-operating curve (AUROC) analysis was conducted to assess clinical performance. Results: The VEGFA rs881858 was significantly associated with BRONJ development. The odds of BRONJ development were 6.45 times (95% CI, 1.69-24.65) higher among carriers of the wild-type rs881858 allele compared with variant homozygote carriers after adjusting for covariates. Additionally, variant homozygote (GG) carriers of rs10434 had higher odds than those with wild-type allele (OR, 3.16). Age >= 65 years (OR, 16.05) and bisphosphonate exposure >= 36 months (OR, 3.67) were also significant risk factors for BRONJ occurrence. AUROC values were higher than 0.78 for all machine learning methods employed in this study. Conclusion: Our study showed that the BRONJ occurrence was associated with VEGFA polymorphisms in osteoporotic women. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.title | Machine Learning Approaches for Predicting Bisphosphonate-Related Osteonecrosis in Women with Osteoporosis Using VEGFA Gene Polymorphisms | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Chung, Jee-Eun | - |
dc.identifier.doi | 10.3390/jpm11060541 | - |
dc.identifier.scopusid | 2-s2.0-85108696872 | - |
dc.identifier.wosid | 000666063600001 | - |
dc.identifier.bibliographicCitation | JOURNAL OF PERSONALIZED MEDICINE, v.11, no.6, pp.1 - 10 | - |
dc.relation.isPartOf | JOURNAL OF PERSONALIZED MEDICINE | - |
dc.citation.title | JOURNAL OF PERSONALIZED MEDICINE | - |
dc.citation.volume | 11 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 10 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Health Care Sciences & Services | - |
dc.relation.journalResearchArea | General & Internal Medicine | - |
dc.relation.journalWebOfScienceCategory | Health Care Sciences & Services | - |
dc.relation.journalWebOfScienceCategory | Medicine, General & Internal | - |
dc.subject.keywordPlus | ENDOTHELIAL-GROWTH-FACTOR | - |
dc.subject.keywordPlus | MEDICATION-RELATED OSTEONECROSIS | - |
dc.subject.keywordPlus | MULTIPLE-MYELOMA | - |
dc.subject.keywordPlus | RISK-FACTORS | - |
dc.subject.keywordPlus | JAW | - |
dc.subject.keywordPlus | PHARMACOGENETICS | - |
dc.subject.keywordPlus | PREECLAMPSIA | - |
dc.subject.keywordPlus | ASSOCIATION | - |
dc.subject.keywordPlus | PREVENTION | - |
dc.subject.keywordAuthor | bisphosphonate-related osteonecrosis | - |
dc.subject.keywordAuthor | VEGFA | - |
dc.subject.keywordAuthor | gene polymorphism | - |
dc.subject.keywordAuthor | machine learning | - |
dc.identifier.url | https://www.mdpi.com/2075-4426/11/6/541 | - |
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