Neural network to predict probabilistically possible mutations in hemagglutinins from Eurasia H1 influenza A virus
2nd International Conference on Computer Vision, Image, and Deep Learning
; 11911, 2021.
Article
in English
| Scopus | ID: covidwho-1511402
ABSTRACT
The current COVID-19 pandemic continues with its new variants, whose mutations are unpredictable. Thus, how to predict mutations in viruses has profound meanings for vaccine and drug development as well as prevention measures. Currently the documented mutations in SARS-CoV-2 are not abundant yet, especially for making phylogenetic tree, it would be useful and easy to use the virus data with abundant mutations such as influenza A virus to build predictive model. In this study, a neural network with feedforward backpropagation algorithm is employed to predict the probabilistically possible mutation positions and mutated amino acids in hemagglutinins from Eurasia H1 influenza A virus. The study demonstrates an encouraging result and suggests the possibility to continue working along this research line. © 2021 SPIE.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
2nd International Conference on Computer Vision, Image, and Deep Learning
Year:
2021
Document Type:
Article
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