DC Field | Value | Language |
---|---|---|
dc.contributor.author | S Nam | - |
dc.contributor.author | S Lee | - |
dc.contributor.author | S Park | - |
dc.contributor.author | Jinhyuk Lee | - |
dc.contributor.author | A Park | - |
dc.contributor.author | Y H Kim | - |
dc.contributor.author | T Park | - |
dc.date.accessioned | 2022-03-22T15:31:46Z | - |
dc.date.available | 2022-03-22T15:31:46Z | - |
dc.date.issued | 2022 | - |
dc.identifier.issn | 1367-4803 | - |
dc.identifier.uri | https://oak.kribb.re.kr/handle/201005/25620 | - |
dc.description.abstract | Motivation: Drug repositioning reveals novel indications for existing drugs and in particular, diseases with no available drugs. Diverse computational drug repositioning methods have been proposed by measuring either drug-treated gene expression signatures or the proximity of drug targets and disease proteins found in prior networks. However, these methods do not explain which signaling subparts allow potential drugs to be selected, and do not consider polypharmacology, i.e. multiple targets of a known drug, in specific subparts. Results: Here, to address the limitations, we developed a subpathway-based polypharmacology drug repositioning method, PATHOME-Drug, based on drug-associated transcriptomes. Specifically, this tool locates subparts of signaling cascading related to phenotype changes (e.g. disease status changes), and identifies existing approved drugs such that their multiple targets are enriched in the subparts. We show that our method demonstrated better performance for detecting signaling context and specific drugs/compounds, compared to WebGestalt and clusterProfiler, for both real biological and simulated datasets. We believe that our tool can successfully address the current shortage of targeted therapy agents. | - |
dc.publisher | Oxford Univ Press | - |
dc.title | PATHOME-Drug: a subpathway-based poly-pharmacology drug-repositioning method | - |
dc.title.alternative | PATHOME-Drug: a subpathway-based poly-pharmacology drug-repositioning method | - |
dc.type | Article | - |
dc.citation.title | Bioinformatics | - |
dc.citation.number | 2 | - |
dc.citation.endPage | 452 | - |
dc.citation.startPage | 444 | - |
dc.citation.volume | 38 | - |
dc.contributor.affiliatedAuthor | Jinhyuk Lee | - |
dc.contributor.alternativeName | 남승윤 | - |
dc.contributor.alternativeName | 이성영 | - |
dc.contributor.alternativeName | 박성진 | - |
dc.contributor.alternativeName | 이진혁 | - |
dc.contributor.alternativeName | 박아론 | - |
dc.contributor.alternativeName | 김연희 | - |
dc.contributor.alternativeName | 박태성 | - |
dc.identifier.bibliographicCitation | Bioinformatics, vol. 38, no. 2, pp. 444-452 | - |
dc.identifier.doi | 10.1093/bioinformatics/btab566 | - |
dc.description.journalClass | Y | - |
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