Détail du document
Identifiant
oai:arXiv.org:2206.03823
Sujet
Quantitative Biology - Quantitativ... Computer Science - Machine Learnin...Auteur
Xu, Yanhua Wojtczak, DominikCatégorie
Computer Science
Année
2022
Date de référencement
03/08/2022
Mots clés
virus set complete sequences protein influenzaRésumé
Influenza occurs every season and occasionally causes pandemics.
Despite its low mortality rate, influenza is a major public health concern, as it can be complicated by severe diseases like pneumonia.
A fast, accurate and low-cost method to predict the origin host and subtype of influenza viruses could help reduce virus transmission and benefit resource-poor areas.
In this work, we propose multi-channel neural networks to predict antigenic types and hosts of influenza A viruses with hemagglutinin and neuraminidase protein sequences.
An integrated data set containing complete protein sequences were used to produce a pre-trained model, and two other data sets were used for testing the model's performance.
One test set contained complete protein sequences, and another test set contained incomplete protein sequences.
The results suggest that multi-channel neural networks are applicable and promising for predicting influenza A virus hosts and antigenic subtypes with complete and partial protein sequences.
;Comment: Accepted for publication at IC3K (KDIR) 2022
Xu, Yanhua,Wojtczak, Dominik, 2022, Multi-channel neural networks for predicting influenza A virus hosts and antigenic types
Investigating the Serotonergic Modulation of Orientation Tuning of Neurons in Primary Visual Cortex of Anesthetized Mice
cortical tuning mean serotonin firing modulation rate neurons orientation visual activity system
