MLb-LDLr: A Machine Learning Model for Predicting the Pathogenicity of LDLr Missense Variants
dc.contributor.author | Larrea Sebal, Asier | |
dc.contributor.author | Benito Vicente, Asier | |
dc.contributor.author | Fernández Higuero, José Ángel | |
dc.contributor.author | Jebari Benslaiman, Shifa | |
dc.contributor.author | Galicia García, Unai | |
dc.contributor.author | Belloso Uribe, Kepa | |
dc.contributor.author | Cenarro Lagunas, Ana | |
dc.contributor.author | Ostolaza Echabe, Elena Amaya | |
dc.contributor.author | Civeira Murillo, Fernando | |
dc.contributor.author | Arrasate Gil, Sonia | |
dc.contributor.author | González Díaz, Humberto | |
dc.contributor.author | Martín Plágaro, César Augusto | |
dc.date.accessioned | 2024-02-05T17:21:43Z | |
dc.date.available | 2024-02-05T17:21:43Z | |
dc.date.issued | 2021-11 | |
dc.identifier.citation | JACC: Basic to Translational Science 6(11) : 815-827 (2021) | es_ES |
dc.identifier.issn | 2452-302X | |
dc.identifier.uri | http://hdl.handle.net/10810/64654 | |
dc.description.abstract | Untreated familial hypercholesterolemia (FH) leads to atherosclerosis and early cardiovascular disease. Mutations in the low-density lipoprotein receptor (LDLr) gene constitute the major cause of FH, and the high number of mutations already described in the LDLr makes necessary cascade screening or in vitro functional characterization to provide a definitive diagnosis. Implementation of high-predicting capacity software constitutes a valuable approach for assessing pathogenicity of LDLr variants to help in the early diagnosis and management of FH disease. This work provides a reliable machine learning model to accurately predict the pathogenicity of LDLr missense variants with specificity of 92.5% and sensitivity of 91.6%. | es_ES |
dc.description.sponsorship | This study was supported by grants from the Basque Government (Cesar Martin, Grupos Consolidados IT-1264-19). Mr Larrea-Sebal was supported by a FPI grant from Gobierno Vasco (2019–2020). Dr Benito-Vicente was supported by Programa de especialización de Personal Investigador Doctor en la UPV/EHU (2019) 2019-2020. Dr Galicia-Garcia was supported by Fundación Biofísica Bizkaia. Ms Jebari-Benslaiman was supported by grant PIF (2017–2018), Gobierno Vasco. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | familial hypercholesterolemia | es_ES |
dc.subject | LDL receptor | es_ES |
dc.subject | machine learning software | es_ES |
dc.subject | pathogenicity | es_ES |
dc.subject | prediction | es_ES |
dc.title | MLb-LDLr: A Machine Learning Model for Predicting the Pathogenicity of LDLr Missense Variants | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © 2021 The authors. Published by Elsevier on behalf of the American College of Cardiology Foundation. This is an open access article under the Creative Commons CC-BY-NC-ND license and permits non-commercial use of the work as published, without adaptation or alteration provided the work is fully attributed. | es_ES |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S2452302X21002710 | es_ES |
dc.identifier.doi | 10.1016/j.jacbts.2021.08.009 | |
dc.departamentoes | Química Orgánica e Inorgánica | es_ES |
dc.departamentoes | Bioquímica y biología molecular | es_ES |
dc.departamentoeu | Kimika Organikoa eta Ez-Organikoa | es_ES |
dc.departamentoeu | Biokimika eta biologia molekularra | es_ES |
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Except where otherwise noted, this item's license is described as © 2021 The authors. Published by Elsevier on behalf of the American College of Cardiology Foundation. This is an open access article under the Creative Commons CC-BY-NC-ND license and permits non-commercial use of the work as published, without adaptation or alteration provided the work is fully attributed.