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

• URL: http://arxiv.org/abs/2206.03823v1
• Date: Wed, 8 Jun 2022 11:47:31 GMT
• Title: Multi-channel neural networks for predicting influenza A virus hosts and antigenic types
• Authors: Yanhua Xu and Dominik Wojtczak
• Abstract summary: 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.
• Score: 3.1981440103815717
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Opponent Shaping for Antibody Development [49.26728828005039]
  Anti-viral therapies are typically designed to target only the current strains of a virus. therapy-induced selective pressures act on viruses to drive the emergence of mutated strains, against which initial therapies have reduced efficacy. We build on a computational model of binding between antibodies and viral antigens to implement a genetic simulation of viral evolutionary escape.
  arXiv  Detail & Related papers  (2024-09-16T14:56:27Z)
• MC-NN: An End-to-End Multi-Channel Neural Network Approach for Predicting Influenza A Virus Hosts and Antigenic Types [5.067354030054702]
  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.
  arXiv  Detail & Related papers  (2023-06-08T23:14:39Z)
• 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)
• Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks [71.78900818931847]
  In tasks like node classification, image segmentation, and named-entity recognition we have a classifier that simultaneously outputs multiple predictions. Existing adversarial robustness certificates consider each prediction independently and are thus overly pessimistic for such tasks. We propose the first collective robustness certificate which computes the number of predictions that are simultaneously guaranteed to remain stable under perturbation.
  arXiv  Detail & Related papers  (2023-02-06T14:46:51Z)
• Accurate Virus Identification with Interpretable Raman Signatures by Machine Learning [12.184128048998906]
  We present a machine learning approach for analyzing Raman spectra of human and avian viruses. A Convolutional Neural Network (CNN) classifier specifically designed for spectral data achieves very high accuracy for a variety of virus type or subtype identification tasks.
  arXiv  Detail & Related papers  (2022-06-05T22:31:14Z)
• A multitask transfer learning framework for the prediction of virus-human protein-protein interactions [0.30586855806896046]
  We develop a transfer learning approach that exploits the information of around 24 million protein sequences and the interaction patterns from the human interactome. We employ an additional objective which aims to maximize the probability of observing human protein-protein interactions. Experimental results show that our proposed model works effectively for both virus-human and bacteria-human protein-protein interaction prediction tasks.
  arXiv  Detail & Related papers  (2021-11-26T07:53:51Z)
• Certifiers Make Neural Networks Vulnerable to Availability Attacks [70.69104148250614]
  We show for the first time that fallback strategies can be deliberately triggered by an adversary. In addition to naturally occurring abstains for some inputs and perturbations, the adversary can use training-time attacks to deliberately trigger the fallback. We design two novel availability attacks, which show the practical relevance of these threats.
  arXiv  Detail & Related papers  (2021-08-25T15:49:10Z)
• 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)
• 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.