Please use this identifier to cite or link to this item: https://doi.org/10.2217/pgs.09.12
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dc.titlePredicting potentially functional SNPs in drug-response genes
dc.contributor.authorPang, G.S.Y.
dc.contributor.authorWang, J.
dc.contributor.authorWang, Z.
dc.contributor.authorLee, C.G.L.
dc.date.accessioned2016-12-13T05:39:15Z
dc.date.available2016-12-13T05:39:15Z
dc.date.issued2009
dc.identifier.citationPang, G.S.Y., Wang, J., Wang, Z., Lee, C.G.L. (2009). Predicting potentially functional SNPs in drug-response genes. Pharmacogenomics 10 (4) : 639-653. ScholarBank@NUS Repository. https://doi.org/10.2217/pgs.09.12
dc.identifier.issn14622416
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/133022
dc.description.abstractSNPs are known to contribute to variations in drug response and there are more than 14 million polymorphisms spanning the human genome. However, not all of these SNPs are functional. It would be impractical and costly to evaluate every individual SNP for functionality experimentally. Consequently, one of the major challenges for researchers has been to seek out functional SNPs from all the SNPs in the human genome. In silico or bioinformatic methods are economical, less labor intensive, yet powerful approaches to filter out potentially functional SNPs in drug-response genes for further study. This allows researchers to prioritize which SNPs to subsequently evaluate experimentally for drug-response studies, as well as potentially providing insights into possible mechanisms underlying how SNPs may affect drug-response genes. © 2009 Future Medicine Ltd.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.2217/pgs.09.12
dc.sourceScopus
dc.subjectBioinformatics
dc.subjectDrug response
dc.subjectFunction
dc.subjectPrediction
dc.subjectSNPs
dc.typeReview
dc.contributor.departmentDUKE-NUS GRADUATE MEDICAL SCHOOL S'PORE
dc.description.doi10.2217/pgs.09.12
dc.description.sourcetitlePharmacogenomics
dc.description.volume10
dc.description.issue4
dc.description.page639-653
dc.description.codenPARMF
dc.identifier.isiut000265727300014
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