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In Silico Simulation of Signal Cascades in Biomedical Networks Based on the Production Rule System
DC Field | Value | Language |
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dc.contributor.author | 김상우 | - |
dc.date.accessioned | 2018-07-20T08:15:39Z | - |
dc.date.available | 2018-07-20T08:15:39Z | - |
dc.date.issued | 2017 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/161021 | - |
dc.description.abstract | Inferring novel findings from known biological knowledge is one of the ultimate goals in systems biology. However, the observation of system-level responses to a given perturbation has not been thoroughly explored due to the lack of proper large-scale inference models. We developed a novel expert system that can be applied to conventional biological networks based on the production rule system which works by transforming networks into a knowledgebase. Testing on large-scale multi-level biomedical networks confirmed the applicability of our system and revealed that hundreds of molecules are affected by the cascades of given signals, thereby activating or repressing key pathways in a cell. | - |
dc.description.statementOfResponsibility | restriction | - |
dc.language | English | - |
dc.publisher | Springer | - |
dc.relation.isPartOf | Lecture Notes in Computer Science | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.rights | https://creativecommons.org/licenses/by-nc-nd/2.0/kr/ | - |
dc.title | In Silico Simulation of Signal Cascades in Biomedical Networks Based on the Production Rule System | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine | - |
dc.contributor.department | Dept. of Life Science | - |
dc.contributor.googleauthor | Sangwoo Kim | - |
dc.contributor.googleauthor | Ho Jung nam | - |
dc.identifier.doi | 10.1007/978-3-319-59575-7_34 | - |
dc.contributor.localId | A00524 | - |
dc.relation.journalcode | J02160 | - |
dc.identifier.url | https://link.springer.com/chapter/10.1007/978-3-319-59575-7_34 | - |
dc.subject.keyword | Network simulation | - |
dc.subject.keyword | Expert system | - |
dc.subject.keyword | Production system | - |
dc.contributor.alternativeName | Kim, Sang Woo | - |
dc.contributor.affiliatedAuthor | Kim, Sang Woo | - |
dc.citation.volume | 10330 | - |
dc.citation.startPage | 356 | - |
dc.citation.endPage | 361 | - |
dc.identifier.bibliographicCitation | Lecture Notes in Computer Science, Vol.10330 : 356-361, 2017 | - |
dc.identifier.rimsid | 60912 | - |
dc.type.rims | ART | - |
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