A comprehensive evaluation of regression-based drug responsiveness prediction models, using cell viability inhibitory concentrations (IC50 values)
DC Field | Value | Language |
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dc.contributor.author | Park, Aron | - |
dc.contributor.author | Joo, Minjae | - |
dc.contributor.author | Kim, Kyungdoc | - |
dc.contributor.author | Son, Won-Joon | - |
dc.contributor.author | Lim, GyuTae | - |
dc.contributor.author | Lee, Jinhyuk | - |
dc.contributor.author | Kim, Jung Ho | - |
dc.contributor.author | Lee, Dae Ho | - |
dc.contributor.author | Nam, Seungyoon | - |
dc.date.accessioned | 2022-05-19T01:40:06Z | - |
dc.date.available | 2022-05-19T01:40:06Z | - |
dc.date.created | 2022-05-12 | - |
dc.date.issued | 2022-04 | - |
dc.identifier.issn | 1367-4803 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/84356 | - |
dc.description.abstract | Motivation: Predicting drug response is critical for precision medicine. Diverse methods have predicted drug responsiveness, as measured by the half-maximal drug inhibitory concentration (IC50), in cultured cells. Although IC5Os are continuous, traditional prediction models have dealt mainly with binary classification of responsiveness. However, since there are few regression-based IC50 predictions, comprehensive evaluations of regression-based IC50 prediction models, including machine learning (ML) and deep learning (DL), for diverse data types and dataset sizes, have not been addressed. Results: Here, we constructed 11 input data settings, including multi-omics settings, with varying dataset sizes, then evaluated the performance of regression-based ML and DL models to predict IC50s. DL models considered two convolutional neural network architectures: CDRScan and residual neural network (ResNet). ResNet was introduced in regression-based DL models for predicting drug response for the first time. As a result, DL models performed better than ML models in all the settings. Also, ResNet performed better than or comparable to CDRScan and ML models in all settings. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | OXFORD UNIV PRESS | - |
dc.relation.isPartOf | BIOINFORMATICS | - |
dc.title | A comprehensive evaluation of regression-based drug responsiveness prediction models, using cell viability inhibitory concentrations (IC50 values) | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000786351100001 | - |
dc.identifier.doi | 10.1093/bioinformatics/btac177 | - |
dc.identifier.bibliographicCitation | BIOINFORMATICS, v.38, no.10, pp.2810 - 2817 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85132361068 | - |
dc.citation.endPage | 2817 | - |
dc.citation.startPage | 2810 | - |
dc.citation.title | BIOINFORMATICS | - |
dc.citation.volume | 38 | - |
dc.citation.number | 10 | - |
dc.contributor.affiliatedAuthor | Park, Aron | - |
dc.contributor.affiliatedAuthor | Joo, Minjae | - |
dc.contributor.affiliatedAuthor | Kim, Jung Ho | - |
dc.contributor.affiliatedAuthor | Lee, Dae Ho | - |
dc.contributor.affiliatedAuthor | Nam, Seungyoon | - |
dc.type.docType | Article; Early Access | - |
dc.subject.keywordPlus | SENSITIVITY | - |
dc.relation.journalResearchArea | Biochemistry & Molecular Biology | - |
dc.relation.journalResearchArea | Biotechnology & Applied Microbiology | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Biochemical Research Methods | - |
dc.relation.journalWebOfScienceCategory | Biotechnology & Applied Microbiology | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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