Détail du document
Identifiant
oai:arXiv.org:2306.05587
Sujet
Computer Science - Machine Learnin... Quantitative Biology - Quantitativ...Auteur
Xu, Yanhua Wojtczak, DominikCatégorie
Computer Science
Année
2023
Date de référencement
28/02/2024
Mots clés
host viruses computer science protein data influenza sequencesRésumé
Influenza poses a significant threat to public health, particularly among the elderly, young children, and people with underlying dis-eases.
The manifestation of severe conditions, such as pneumonia, highlights the importance of preventing the spread of influenza.
An accurate and cost-effective prediction of the host and antigenic sub-types of influenza A viruses is essential to addressing this issue, particularly in resource-constrained regions.
In this study, we propose a multi-channel neural network model to predict the host and antigenic subtypes of influenza A viruses from hemagglutinin and neuraminidase protein sequences.
Our model was trained on a comprehensive data set of complete protein sequences and evaluated on various test data sets of complete and incomplete sequences.
The results demonstrate the potential and practicality of using multi-channel neural networks in predicting the host and antigenic subtypes of influenza A viruses from both full and partial protein sequences.
;Comment: Accepted version submitted to the SN Computer Science; Published in the SN Computer Science 2023; V2: minor updates were made to the Results section; V3: minor updates regarding data description; V4: correct the time stamps mentioned in the legends of Figures 1 and 2
Xu, Yanhua,Wojtczak, Dominik, 2023, MC-NN: An End-to-End Multi-Channel Neural Network Approach for Predicting Influenza A Virus Hosts and Antigenic Types
Configurable Safety Tuning of Language Models with Synthetic Preference Data
data preference cst dpo configurable language safety
Affective flexibility as a developmental building block of cognitive reappraisal: An fMRI study
cognitive success affective flexibility
