مورد إلكتروني
MLb-LDLr: A Machine Learning Model for Predicting the Pathogenicity of LDLr Missense Variants
| العنوان: | MLb-LDLr: A Machine Learning Model for Predicting the Pathogenicity of LDLr Missense Variants |
|---|---|
| بيانات النشر: | Elsevier American College of Cardiology Foundation 2021-11 |
| تفاصيل مُضافة: | Eusko Jaurlaritza Universidad del País Vasco Fundación Biofísica Bizkaia Larrea, Asier Benito-Vicente, Asier Fernández-Higuero, José Ángel Jebari Benslaiman, Shifa Galicia-García, Unai Uribe, Kepa B. Cenarro, Ana Ostolaza, Helena Civeira, Fernando Arrasate, Sonia González-Díaz, Humberto Martín, César |
| نوع الوثيقة: | Electronic Resource |
| 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%. |
| مصطلحات الفهرس: | 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, artículo |
| URL: | Publisher's version Sí |
| الإتاحة: | Open access content. Open access content https://creativecommons.org/licenses/by-nc-nd/4.0 openAccess |
| ملاحظة: | English |
| أرقام أخرى: | CTK oai:digital.csic.es:10261/311418 JACC - Basic to Translational Science 6(11): 815-827 (2021) 10.1016/j.jacbts.2021.08.009 2452-302X 34869944 2-s2.0-85118822550 1395197960 |
| المصدر المساهم: | CSIC From OAIster®, provided by the OCLC Cooperative. |
| رقم الأكسشن: | edsoai.on1395197960 |
| قاعدة البيانات: | OAIster |
| الوصف غير متاح. |