Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 주재열 | - |
dc.date.accessioned | 2022-04-15T07:40:58Z | - |
dc.date.available | 2022-04-15T07:40:58Z | - |
dc.date.issued | 2021-10 | - |
dc.identifier.citation | Advances in Biological Regulation | en_US |
dc.identifier.issn | 2212-4926 | - |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S221249262100049X | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/170053 | - |
dc.description.abstract | Genetic mutations leading to the development of various diseases, such as cancer, diabetes, and neurodegenerative disorders, can be attributed to multiple mechanisms and exposure to diverse environments. These disorders further increase gene mutation rates and affect the activity of translated proteins, both phenomena associated with cellular responses. Therefore, maintaining the integrity of genetic and epigenetic information is critical for disease suppression and prevention. With the advent of genome sequencing technologies, large-scale genomic data-based machine learning tools, including deep learning, have been used to predict and identify somatic inactivation or negative dominant expression of target genes in various diseases. Although deep learning studies have recently been highlighted for their ability to distinguish between the genetic information of diseases, conventional wisdom is also necessary to explain the correlation between genotype and phenotype. Herein, we summarize the current understanding of phosphoinositide-specific phospholipase C isozymes (PLCs) and an overview of their associations with genetic variation, as well as their emerging roles in several diseases. We also predicted and discussed new findings of cryptic PLC splice variants by deep learning and the clinical implications of the PLC genetic variations predicted using these tools. | en_US |
dc.description.sponsorship | We would like to thank Dr. Mingon Kang (University of Nevada, Las Vegas) for assistance of Splice-AI analysis. This work was supported by Korea Brain Research Institute (KBRI) basic research program through KBRI funded by the Ministry of Science and ICT (21-BR-02-09, 21-BR-02-21), and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2019R1F1A1059595). A graphic figure was made with biorender.com. We would like to thank Editage for English language editing. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Elsevier Ltd. | en_US |
dc.subject | Brain disorders | en_US |
dc.subject | Deep learning | en_US |
dc.subject | PLCs | en_US |
dc.subject | Cryptic splice variants | en_US |
dc.title | Prediction of genetic alteration of Phospholipase C Isozymes in Brain disorders: Studies with deep learning | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1016/j.jbior.2021.100833 | - |
dc.relation.journal | Advances in Biological Regulation | - |
dc.contributor.googleauthor | Joo, Jae-Yeol | - |
dc.contributor.googleauthor | Lim, Key-Hwan | - |
dc.contributor.googleauthor | Yang, Sumin | - |
dc.contributor.googleauthor | Kim, Sung-Hyun | - |
dc.contributor.googleauthor | Cocco, Lucio | - |
dc.contributor.googleauthor | Suh, Pann-Ghill | - |
dc.relation.code | 2021012704 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF PHARMACY[E] | - |
dc.sector.department | DEPARTMENT OF PHARMACY | - |
dc.identifier.pid | joojy | - |
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