Veterinary systems biology for bridging the phenotype–genotype gap via computational modeling for disease epidemiology and animal welfare
DC Field | Value | Language |
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dc.contributor.author | Kumar, Rajesh | - |
dc.contributor.author | Kim, Jun-Mo | - |
dc.date.accessioned | 2024-03-19T06:30:33Z | - |
dc.date.available | 2024-03-19T06:30:33Z | - |
dc.date.issued | 2024-01 | - |
dc.identifier.issn | 1467-5463 | - |
dc.identifier.issn | 1477-4054 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/72920 | - |
dc.description.abstract | Veterinary systems biology is an innovative approach that integrates biological data at the molecular and cellular levels, allowing for a more extensive understanding of the interactions and functions of complex biological systems in livestock and veterinary science. It has tremendous potential to integrate multi-omics data with the support of vetinformatics resources for bridging the phenotype–genotype gap via computational modeling. To understand the dynamic behaviors of complex systems, computational models are frequently used. It facilitates a comprehensive understanding of how a host system defends itself against a pathogen attack or operates when the pathogen compromises the host’s immune system. In this context, various approaches, such as systems immunology, network pharmacology, vaccinology and immunoinformatics, can be employed to effectively investigate vaccines and drugs. By utilizing this approach, we can ensure the health of livestock. This is beneficial not only for animal welfare but also for human health and environmental well-being. Therefore, the current review offers a detailed summary of systems biology advancements utilized in veterinary sciences, demonstrating the potential of the holistic approach in disease epidemiology, animal welfare and productivity. © 2024 Oxford University Press. All rights reserved. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Oxford University Press | - |
dc.title | Veterinary systems biology for bridging the phenotype–genotype gap via computational modeling for disease epidemiology and animal welfare | - |
dc.type | Article | - |
dc.identifier.doi | 10.1093/bib/bbae025 | - |
dc.identifier.bibliographicCitation | Briefings in Bioinformatics, v.25, no.2 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.wosid | 001177227400046 | - |
dc.identifier.scopusid | 2-s2.0-85184913349 | - |
dc.citation.number | 2 | - |
dc.citation.title | Briefings in Bioinformatics | - |
dc.citation.volume | 25 | - |
dc.type.docType | Review | - |
dc.publisher.location | 영국 | - |
dc.subject.keywordAuthor | computational models | - |
dc.subject.keywordAuthor | drug discovery | - |
dc.subject.keywordAuthor | multi-omics | - |
dc.subject.keywordAuthor | network analysis | - |
dc.subject.keywordAuthor | systems vaccinology | - |
dc.subject.keywordAuthor | veterinary systems biology | - |
dc.subject.keywordPlus | MULTI-OMICS INTEGRATION | - |
dc.subject.keywordPlus | GENE-EXPRESSION | - |
dc.subject.keywordPlus | DATABASE | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.subject.keywordPlus | ADJUVANTS | - |
dc.subject.keywordPlus | LIVESTOCK | - |
dc.subject.keywordPlus | ACCURACY | - |
dc.subject.keywordPlus | NETWORKS | - |
dc.relation.journalResearchArea | Biochemistry & Molecular Biology | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Biochemical Research Methods | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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