MLb-LDLr: A Machine Learning Model for Predicting the Pathogenicity of LDLr Missense Variants
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...
| Autores: | , , , , , , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/311418 |
| Acceso en línea: | http://hdl.handle.net/10261/311418 https://api.elsevier.com/content/abstract/scopus_id/85118822550 |
| Access Level: | acceso abierto |
| Palabra clave: | 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 |
| Sumario: | 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%. |
|---|