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Found 5 papers, 0 papers with code
MC-NN: An End-to-End Multi-Channel Neural Network Approach for Predicting Influenza A Virus Hosts and Antigenic Types
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.
Machine Learning for Flow Cytometry Data Analysis
This thesis mainly concerns discover the potential shortcoming of current automated-gating algorithms in both real datasets and synthetic datasets.
Dive into Machine Learning Algorithms for Influenza Virus Host Prediction with Hemagglutinin Sequences
Influenza viruses mutate rapidly and can pose a threat to public health, especially to those in vulnerable groups.
Multi-channel neural networks for predicting influenza A virus hosts and antigenic types
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.
Predicting Influenza A Viral Host Using PSSM and Word Embeddings
The MCC obtained using the model with word embedding is around 96%, and the F1 is around 97%.
