التفاصيل البيبلوغرافية
| العنوان: |
Multi-task learning for predicting SARS-CoV-2 antibody escape |
| المؤلفون: |
Barak Gross, Roded Sharan |
| المصدر: |
Frontiers in Genetics, Vol 13 (2022) |
| بيانات النشر: |
Frontiers Media S.A., 2022. |
| سنة النشر: |
2022 |
| المجموعة: |
LCC:Genetics |
| مصطلحات موضوعية: |
multi-task learning, neural network, escape prediction, coronavirus, receptor binding domain, Genetics, QH426-470 |
| الوصف: |
The coronavirus pandemic has revolutionized our world, with vaccination proving to be a key tool in fighting the disease. However, a major threat to this line of attack are variants that can evade the vaccine. Thus, a fundamental problem of growing importance is the identification of mutations of concern with high escape probability. In this paper we develop a computational framework that harnesses systematic mutation screens in the receptor binding domain of the viral Spike protein for escape prediction. The framework analyzes data on escape from multiple antibodies simultaneously, creating a latent representation of mutations that is shown to be effective in predicting escape and binding properties of the virus. We use this representation to validate the escape potential of current SARS-CoV-2 variants. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
1664-8021 |
| Relation: |
https://www.frontiersin.org/articles/10.3389/fgene.2022.886649/full; https://doaj.org/toc/1664-8021 |
| DOI: |
10.3389/fgene.2022.886649 |
| URL الوصول: |
https://doaj.org/article/5a18e1ec506846c9b0fc0020fbf82356 |
| رقم الانضمام: |
edsdoj.5a18e1ec506846c9b0fc0020fbf82356 |
| قاعدة البيانات: |
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