DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Yunseong | ko |
dc.contributor.author | Han, Younghyun | ko |
dc.contributor.author | Hopper, Corbin | ko |
dc.contributor.author | Lee, Jonghoon | ko |
dc.contributor.author | Joo, Jae Il | ko |
dc.contributor.author | Gong, Jeong-Ryeol | ko |
dc.contributor.author | Lee, Chun-Kyung | ko |
dc.contributor.author | Jang, Seong-Hoon | ko |
dc.contributor.author | Kang, Junsoo | ko |
dc.contributor.author | Kim, Taeyoung | ko |
dc.contributor.author | Cho, Kwang-Hyun | ko |
dc.date.accessioned | 2024-06-11T15:00:08Z | - |
dc.date.available | 2024-06-11T15:00:08Z | - |
dc.date.created | 2024-06-11 | - |
dc.date.created | 2024-06-11 | - |
dc.date.created | 2024-06-11 | - |
dc.date.issued | 2024-05 | - |
dc.identifier.citation | Cell Reports Methods, v.4, no.5 | - |
dc.identifier.issn | 2667-2375 | - |
dc.identifier.uri | http://hdl.handle.net/10203/319744 | - |
dc.description.abstract | Predicting cellular responses to perturbations requires interpretable insights into molecular regulatory dynamics to perform reliable cell fate control, despite the confounding non-linearity of the underlying interactions. There is a growing interest in developing machine learning-based perturbation response prediction models to handle the non-linearity of perturbation data, but their interpretation in terms of molecular regulatory dynamics remains a challenge. Alternatively, for meaningful biological interpretation, logical network models such as Boolean networks are widely used in systems biology to represent intracellular molecular regulation. However, determining the appropriate regulatory logic of large-scale networks remains an obstacle due to the high-dimensional and discontinuous search space. To tackle these challenges, we present a scalable derivative-free optimizer trained by meta-reinforcement learning for Boolean network models. The logical network model optimized by the trained optimizer successfully predicts anti-cancer drug responses of cancer cell lines, while simultaneously providing insight into their underlying molecular regulatory mechanisms. | - |
dc.language | English | - |
dc.publisher | Cell Press | - |
dc.title | A grey box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations | - |
dc.type | Article | - |
dc.identifier.scopusid | 2-s2.0-85192869065 | - |
dc.type.rims | ART | - |
dc.citation.volume | 4 | - |
dc.citation.issue | 5 | - |
dc.citation.publicationname | Cell Reports Methods | - |
dc.identifier.doi | 10.1016/j.crmeth.2024.100773 | - |
dc.contributor.localauthor | Cho, Kwang-Hyun | - |
dc.contributor.nonIdAuthor | Kim, Yunseong | - |
dc.contributor.nonIdAuthor | Han, Younghyun | - |
dc.contributor.nonIdAuthor | Hopper, Corbin | - |
dc.contributor.nonIdAuthor | Lee, Jonghoon | - |
dc.contributor.nonIdAuthor | Joo, Jae Il | - |
dc.contributor.nonIdAuthor | Gong, Jeong-Ryeol | - |
dc.contributor.nonIdAuthor | Lee, Chun-Kyung | - |
dc.contributor.nonIdAuthor | Jang, Seong-Hoon | - |
dc.contributor.nonIdAuthor | Kang, Junsoo | - |
dc.contributor.nonIdAuthor | Kim, Taeyoung | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
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