DeepAI AI Chat
Log In Sign Up

Dive into Machine Learning Algorithms for Influenza Virus Host Prediction with Hemagglutinin Sequences

07/28/2022
by   Yanhua Xu, et al.
University of Liverpool
0

Influenza viruses mutate rapidly and can pose a threat to public health, especially to those in vulnerable groups. Throughout history, influenza A viruses have caused pandemics between different species. It is important to identify the origin of a virus in order to prevent the spread of an outbreak. Recently, there has been increasing interest in using machine learning algorithms to provide fast and accurate predictions for viral sequences. In this study, real testing data sets and a variety of evaluation metrics were used to evaluate machine learning algorithms at different taxonomic levels. As hemagglutinin is the major protein in the immune response, only hemagglutinin sequences were used and represented by position-specific scoring matrix and word embedding. The results suggest that the 5-grams-transformer neural network is the most effective algorithm for predicting viral sequence origins, with approximately 99.54 classification level, and approximately 94.74 80.79

READ FULL TEXT

page 10

page 11

01/04/2022

Predicting Influenza A Viral Host Using PSSM and Word Embeddings

The rapid mutation of the influenza virus threatens public health. Reass...
06/08/2022

Multi-channel neural networks for predicting influenza A virus hosts and antigenic types

Influenza occurs every season and occasionally causes pandemics. Despite...
01/28/2017

Deep Recurrent Neural Network for Protein Function Prediction from Sequence

As high-throughput biological sequencing becomes faster and cheaper, the...
08/09/2021

Classification of Influenza Hemagglutinin Protein Sequences using Convolutional Neural Networks

The Influenza virus can be considered as one of the most severe viruses ...
03/08/2020

ASAP-SML: An Antibody Sequence Analysis Pipeline Using Statistical Testing and Machine Learning

Antibodies are capable of potently and specifically binding individual a...