دورية أكاديمية
أعرض في EDS

MLb-LDLr: A Machine Learning Model for Predicting the Pathogenicity of LDLr Missense Variants.

التفاصيل البيبلوغرافية
العنوان: MLb-LDLr: A Machine Learning Model for Predicting the Pathogenicity of LDLr Missense Variants.
المؤلفون: Larrea-Sebal A; Fundación Biofísica Bizkaia, Leioa, Spain.; Instituto Biofisika (UPV/EHU, CSIC), University of the Basque Country, Leioa, Spain., Benito-Vicente A; Instituto Biofisika (UPV/EHU, CSIC), University of the Basque Country, Leioa, Spain.; Department of Biochemistry and Molecular Biology, University of the Basque Country, Leioa, Spain., Fernandez-Higuero JA; Department of Biochemistry and Molecular Biology, University of the Basque Country, Leioa, Spain., Jebari-Benslaiman S; Instituto Biofisika (UPV/EHU, CSIC), University of the Basque Country, Leioa, Spain.; Department of Biochemistry and Molecular Biology, University of the Basque Country, Leioa, Spain., Galicia-Garcia U; Fundación Biofísica Bizkaia, Leioa, Spain.; Instituto Biofisika (UPV/EHU, CSIC), University of the Basque Country, Leioa, Spain., Uribe KB; Center for Cooperative Research in Biomaterials (CIC biomaGUNE), Basque Research and Technology Alliance (BRTA), Donostia San Sebastián, Spain., Cenarro A; Lipid Unit, Hospital Universitario Miguel Servet, IIS Aragon, CIBERCV, Universidad de Zaragoza, Spain., Ostolaza H; Instituto Biofisika (UPV/EHU, CSIC), University of the Basque Country, Leioa, Spain.; Department of Biochemistry and Molecular Biology, University of the Basque Country, Leioa, Spain., Civeira F; Lipid Unit, Hospital Universitario Miguel Servet, IIS Aragon, CIBERCV, Universidad de Zaragoza, Spain., Arrasate S; Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa, Spain., González-Díaz H; Instituto Biofisika (UPV/EHU, CSIC), University of the Basque Country, Leioa, Spain.; IKERBASQUE, Basque Foundation for Science, Bilbao, Spain., Martín C; Instituto Biofisika (UPV/EHU, CSIC), University of the Basque Country, Leioa, Spain.; Department of Biochemistry and Molecular Biology, University of the Basque Country, Leioa, Spain.
المصدر: JACC. Basic to translational science [JACC Basic Transl Sci] 2021 Nov 22; Vol. 6 (11), pp. 815-827. Date of Electronic Publication: 2021 Nov 22 (Print Publication: 2021).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Elsevier on behalf of the American College of Cardiology Foundation Country of Publication: United States NLM ID: 101677259 Publication Model: eCollection Cited Medium: Internet ISSN: 2452-302X (Electronic) Linking ISSN: 2452302X NLM ISO Abbreviation: JACC Basic Transl Sci Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: [New York] : Elsevier on behalf of the American College of Cardiology Foundation, [2016]-
مستخلص: 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%.
Competing Interests: 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.
(© 2021 The Authors.)
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فهرسة مساهمة: Keywords: ANN, artificial neural network; AUROC, area under the receiver operating curve; EGS, expert-guided selection; ESEA, Excel Solver Evolutionary algorithm; FH, familial hypercholesterolemia; LDA, linear discriminant analysis; LDL receptor; LDL, low-density lipoprotein; LDLr, low-density lipoprotein receptor; LNN, linear neural networks; ML, machine learning; MLP, multilayer perceptron; MLb-LDLr, machine-learning–based low-density lipoprotein receptor software; RBF, radial basis function; UTR, untranslated region; familial hypercholesterolemia; machine learning software; pathogenicity; prediction
تواريخ الأحداث: Date Created: 20211206 Latest Revision: 20211207
رمز التحديث: 20240628
مُعرف محوري في PubMed: PMC8617597
DOI: 10.1016/j.jacbts.2021.08.009
PMID: 34869944
قاعدة البيانات: MEDLINE
الوصف
تدمد:2452-302X
DOI:10.1016/j.jacbts.2021.08.009