Predicting mechanism of action of novel compounds using compound structure and transcriptomic signature coembedding
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jang, Gwanghoon | - |
dc.contributor.author | Park, Sungjoon | - |
dc.contributor.author | Lee, Sanghoon | - |
dc.contributor.author | Kim, Sunkyu | - |
dc.contributor.author | Park, Sejeong | - |
dc.contributor.author | Kang, Jaewoo | - |
dc.date.accessioned | 2022-02-28T14:41:43Z | - |
dc.date.available | 2022-02-28T14:41:43Z | - |
dc.date.created | 2022-01-20 | - |
dc.date.issued | 2021-07 | - |
dc.identifier.issn | 1367-4803 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/137262 | - |
dc.description.abstract | Motivation: Identifying mechanism of actions (MoA) of novel compounds is crucial in drug discovery. Careful understanding of MoA can avoid potential side effects of drug candidates. Efforts have been made to identify MoA using the transcriptomic signatures induced by compounds. However, these approaches fail to reveal MoAs in the absence of actual compound signatures. Results: We present MoAble, which predicts MoAs without requiring compound signatures. We train a deep learning-based coembedding model to map compound signatures and compound structure into the same embedding space. The model generates low-dimensional compound signature representation from the compound structures. To predict MoAs, pathway enrichment analysis is performed based on the connectivity between embedding vectors of compounds and those of genetic perturbation. Results show that MoAble is comparable to the methods that use actual compound signatures. We demonstrate that MoAble can be used to reveal MoAs of novel compounds without measuring compound signatures with the same prediction accuracy as that with measuring them. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | OXFORD UNIV PRESS | - |
dc.subject | SET ENRICHMENT ANALYSIS | - |
dc.subject | CONNECTIVITY MAP | - |
dc.subject | GENE-EXPRESSION | - |
dc.subject | WEB SERVER | - |
dc.title | Predicting mechanism of action of novel compounds using compound structure and transcriptomic signature coembedding | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kang, Jaewoo | - |
dc.identifier.doi | 10.1093/bioinformatics/btab275 | - |
dc.identifier.scopusid | 2-s2.0-85111438670 | - |
dc.identifier.wosid | 000697703700045 | - |
dc.identifier.bibliographicCitation | BIOINFORMATICS, v.37, pp.I376 - I382 | - |
dc.relation.isPartOf | BIOINFORMATICS | - |
dc.citation.title | BIOINFORMATICS | - |
dc.citation.volume | 37 | - |
dc.citation.startPage | I376 | - |
dc.citation.endPage | I382 | - |
dc.type.rims | ART | - |
dc.type.docType | Article; Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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.subject.keywordPlus | SET ENRICHMENT ANALYSIS | - |
dc.subject.keywordPlus | CONNECTIVITY MAP | - |
dc.subject.keywordPlus | GENE-EXPRESSION | - |
dc.subject.keywordPlus | WEB SERVER | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.