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Cryptic mutations of PLC family members in brain disorders: recent discoveries and a deep-learning-based approach

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dc.contributor.authorLim, Key-Hwan-
dc.contributor.authorYang, Sumin-
dc.contributor.authorKim, Sung-Hyun-
dc.contributor.authorKo, Euiseong-
dc.contributor.authorKang, Mingon-
dc.contributor.authorJoo, Jae-Yeol-
dc.date.accessioned2023-08-16T09:29:00Z-
dc.date.available2023-08-16T09:29:00Z-
dc.date.created2023-03-07-
dc.date.issued2023-04-
dc.identifier.issn0006-8950-
dc.identifier.urihttp://scholarworks.bwise.kr/kbri/handle/2023.sw.kbri/177-
dc.description.abstractLim et al. review the mechanisms underlying phospholipase C regulation of the phosphoinositide signalling pathway and the genetic variation of phospholipase C in brain disorders. They discuss the potential for analyses based on deep learning to identify phospholipase C mutations in brain disorders. Phospholipase C (PLC) is an essential isozyme involved in the phosphoinositide signalling pathway, which maintains cellular homeostasis. Gain- and loss-of-function mutations in PLC affect enzymatic activity and are therefore associated with several disorders. Alternative splicing variants of PLC can interfere with complex signalling networks associated with oncogenic transformation and other diseases, including brain disorders. Cells and tissues with various mutations in PLC contribute different phosphoinositide signalling pathways and disease progression, however, identifying cryptic mutations in PLC remains challenging. Herein, we review both the mechanisms underlying PLC regulation of the phosphoinositide signalling pathway and the genetic variation of PLC in several brain disorders. In addition, we discuss the present challenges associated with the potential of deep-learning-based analysis for the identification of PLC mutations in brain disorders.-
dc.language영어-
dc.language.isoen-
dc.publisherOxford University Press-
dc.titleCryptic mutations of PLC family members in brain disorders: recent discoveries and a deep-learning-based approach-
dc.typeArticle-
dc.contributor.affiliatedAuthorLim, Key-Hwan-
dc.contributor.affiliatedAuthorYang, Sumin-
dc.contributor.affiliatedAuthorKim, Sung-Hyun-
dc.contributor.affiliatedAuthorJoo, Jae-Yeol-
dc.identifier.doi10.1093/brain/awac451-
dc.identifier.wosid000926931800001-
dc.identifier.bibliographicCitationBrain, v.146, no.4, pp.1267 - 1280-
dc.relation.isPartOfBrain-
dc.citation.titleBrain-
dc.citation.volume146-
dc.citation.number4-
dc.citation.startPage1267-
dc.citation.endPage1280-
dc.type.rimsART-
dc.type.docTypeReview-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaNeurosciences & Neurology-
dc.relation.journalWebOfScienceCategoryClinical Neurology-
dc.relation.journalWebOfScienceCategoryNeurosciences-
dc.subject.keywordPlusPHOSPHOLIPASE-C-DELTA-
dc.subject.keywordPlusINFANTILE EPILEPTIC ENCEPHALOPATHY-
dc.subject.keywordPlusPLECKSTRIN HOMOLOGY DOMAIN-
dc.subject.keywordPlusAUTISM SPECTRUM DISORDER-
dc.subject.keywordPlusLOSS-OF-FUNCTION-
dc.subject.keywordPlusALZHEIMERS-DISEASE-
dc.subject.keywordPlusGENETIC-VARIATION-
dc.subject.keywordPlusBIPOLAR DISORDER-
dc.subject.keywordPlusPHOSPHATIDYLINOSITOL 4,5-BISPHOSPHATE-
dc.subject.keywordPlusPHYSIOLOGICAL FUNCTIONS-
dc.subject.keywordAuthorbrain disorders-
dc.subject.keywordAuthordeep learning-
dc.subject.keywordAuthorgenetic variation-
dc.subject.keywordAuthorhigh-throughput analysis-
dc.subject.keywordAuthorPLCs-
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