MC-NN: An End-to-End Multi-Channel Neural Network Approach for Predicting Influenza A Virus Hosts and Antigenic Types

• URL: http://arxiv.org/abs/2306.05587v4
• Date: Wed, 21 Feb 2024 22:52:08 GMT
• Title: MC-NN: An End-to-End Multi-Channel Neural Network Approach for Predicting Influenza A Virus Hosts and Antigenic Types
• Authors: Yanhua Xu and Dominik Wojtczak
• Abstract summary: Influenza poses a significant threat to public health, particularly among the elderly, young children, and people with underlying dis-eases. We propose a multi-channel neural network model to predict the host and antigenic sub-types of influenza A viruses.
• Score: 5.067354030054702
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Virus2Vec: Viral Sequence Classification Using Machine Learning [48.40285316053593]
  We propose Virus2Vec, a feature-vector representation for viral sequences that enable machine learning models to identify viral hosts. We empirically evaluate Virus2Vec on real-world spike sequences of Coronaviridae and rabies virus sequence data to predict the host. Our results demonstrate that Virus2Vec outperforms the predictive accuracies of baseline and state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-04-24T08:17:16Z)
• PCD2Vec: A Poisson Correction Distance-Based Approach for Viral Host Classification [0.966840768820136]
  Coronaviruses are membrane-enveloped, non-segmented positive-strand RNA viruses belonging to the Coronaviridae family. In the Coronavirus genome, an essential structural region is the spike region, and it's responsible for attaching the virus to the host cell membrane. We propose a novel method for predicting the host specificity of coronaviruses by analyzing spike protein sequences from different viral subgenera and species.
  arXiv  Detail & Related papers  (2023-04-13T03:02:22Z)
• Data-Centric Epidemic Forecasting: A Survey [56.99209141838794]
  This survey delves into various data-driven methodological and practical advancements. We enumerate the large number of epidemiological datasets and novel data streams that are relevant to epidemic forecasting. We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems.
  arXiv  Detail & Related papers  (2022-07-19T16:15:11Z)
• Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemics [2.705025060422369]
  Infectious diseases remain among the top contributors to human illness and death worldwide. Forecasts of epidemics can assist stakeholders in tailoring countermeasures to the situation at hand.
  arXiv  Detail & Related papers  (2022-06-21T19:31:25Z)
• Multi-channel neural networks for predicting influenza A virus hosts and antigenic types [3.1981440103815717]
  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. We propose multi-channel neural networks to predict antigenic types and hosts of influenza A viruses with complete and partial protein sequences.
  arXiv  Detail & Related papers  (2022-06-08T11:47:31Z)
• Classification of Influenza Hemagglutinin Protein Sequences using Convolutional Neural Networks [8.397189036839956]
  This paper focuses on accurately predicting if an Influenza type A virus can infect specific hosts, and more specifically, Human, Avian and Swine hosts, using only the protein sequence of the HA gene. We propose encoding the protein sequences into numerical signals using the Hydrophobicity Index and subsequently utilising a Convolutional Neural Network-based predictive model. As the results show, the proposed model can distinguish HA protein sequences with high accuracy whenever the virus under investigation can infect Human, Avian or Swine hosts.
  arXiv  Detail & Related papers  (2021-08-09T10:42:26Z)
• A k-mer Based Approach for SARS-CoV-2 Variant Identification [55.78588835407174]
  We show that preserving the order of the amino acids helps the underlying classifiers to achieve better performance. We also show the importance of the different amino acids which play a key role in identifying variants and how they coincide with those reported by the USA's Centers for Disease Control and Prevention (CDC)
  arXiv  Detail & Related papers  (2021-08-07T15:08:15Z)
• Semi-supervised Neural Networks solve an inverse problem for modeling Covid-19 spread [61.9008166652035]
  We study the spread of COVID-19 using a semi-supervised neural network. We assume a passive part of the population remains isolated from the virus dynamics.
  arXiv  Detail & Related papers  (2020-10-10T19:33:53Z)
• Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19 [75.99038202534628]
  We propose CALI-Net, a neural transfer learning architecture which allows us to'steer' a historical disease forecasting model to new scenarios where flu and COVID co-exist. Our experiments demonstrate that our approach is successful in adapting a historical forecasting model to the current pandemic.
  arXiv  Detail & Related papers  (2020-09-23T22:35:43Z)
• MutaGAN: A Seq2seq GAN Framework to Predict Mutations of Evolving Protein Populations [0.0]
  Influenza virus sequences were identified as an ideal test case for this deep learning framework. MutaGAN generated "child" sequences from a given "parent" protein sequence with a median Levenshtein distance of 2.00 amino acids. Results demonstrate the power of the MutaGAN framework to aid in pathogen forecasting with implications for broad utility in evolutionary prediction for any protein population.
  arXiv  Detail & Related papers  (2020-08-26T20:20:30Z)
• Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection [86.81773672627406]
  Clusters of viral pneumonia during a short period of time may be a harbinger of an outbreak or pandemic, like SARS, MERS, and recent COVID-19. Rapid and accurate detection of viral pneumonia using chest X-ray can be significantly useful in large-scale screening and epidemic prevention. Viral pneumonia often have diverse causes and exhibit notably different visual appearances on X-ray images.
  arXiv  Detail & Related papers  (2020-03-27T11:32:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.