Anti-virus Autobots: Predicting More Infectious Virus Variants for
Pandemic Prevention through Deep Learning
- URL: http://arxiv.org/abs/2205.14854v1
- Date: Mon, 30 May 2022 05:04:40 GMT
- Title: Anti-virus Autobots: Predicting More Infectious Virus Variants for
Pandemic Prevention through Deep Learning
- Authors: Glenda Tan Hui En, Koay Tze Erhn, Shen Bingquan
- Abstract summary: More infectious virus variants can arise from rapid mutations in their proteins.
These variants can evade one's immune system and infect vaccinated individuals, lowering vaccine efficacy.
This project proposes Optimus PPIme - a deep learning approach to predict future, more infectious variants from an existing virus.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: More infectious virus variants can arise from rapid mutations in their
proteins, creating new infection waves. These variants can evade one's immune
system and infect vaccinated individuals, lowering vaccine efficacy. Hence, to
improve vaccine design, this project proposes Optimus PPIme - a deep learning
approach to predict future, more infectious variants from an existing virus
(exemplified by SARS-CoV-2). The approach comprises an algorithm which acts as
a "virus" attacking a host cell. To increase infectivity, the "virus" mutates
to bind better to the host's receptor. 2 algorithms were attempted - greedy
search and beam search. The strength of this variant-host binding was then
assessed by a transformer network we developed, with a high accuracy of 90%.
With both components, beam search eventually proposed more infectious variants.
Therefore, this approach can potentially enable researchers to develop vaccines
that provide protection against future infectious variants before they emerge,
pre-empting outbreaks and saving lives.
Related papers
- Vaxformer: Antigenicity-controlled Transformer for Vaccine Design
Against SARS-CoV-2 [0.6850683267295248]
The present study proposes a novel conditional protein Language Model architecture, called Vaxformer.
Vaxformer is designed to produce natural-looking antigenicity-controlled SARS-CoV-2 spike proteins.
arXiv Detail & Related papers (2023-05-18T13:36:57Z) - 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) - Graph Adversarial Immunization for Certifiable Robustness [63.58739705845775]
Graph neural networks (GNNs) are vulnerable to adversarial attacks.
Existing defenses focus on developing adversarial training or model modification.
We propose and formulate graph adversarial immunization, i.e., vaccinating part of graph structure.
arXiv Detail & Related papers (2023-02-16T03:18:43Z) - Dense Feature Memory Augmented Transformers for COVID-19 Vaccination
Search Classification [60.49594822215981]
This paper presents a classification model for detecting COVID-19 vaccination related search queries.
We propose a novel approach of considering dense features as memory tokens that the model can attend to.
We show that this new modeling approach enables a significant improvement to the Vaccine Search Insights (VSI) task.
arXiv Detail & Related papers (2022-12-16T13:57:41Z) - Efficient Cavity Searching for Gene Network of Influenza A Virus [8.690486131601075]
High order structures (cavities and cliques) of the gene network of influenza A virus reveal tight associations among viruses during evolution.
We propose a model named HyperSearch based on deep learning to search cavities in a computable complex network for influenza virus genetics.
arXiv Detail & Related papers (2022-11-05T16:24:55Z) - 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) - PhyloTransformer: A Discriminative Model for Mutation Prediction Based
on a Multi-head Self-attention Mechanism [10.468453827172477]
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused an ongoing pandemic infecting 219 million people as of 10/19/21, with a 3.6% mortality rate.
Here we developed PhyloTransformer, a Transformer-based discriminative model that engages a multi-head self-attention mechanism to model genetic mutations that may lead to viral reproductive advantage.
arXiv Detail & Related papers (2021-11-03T01:30:57Z) - 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) - Predicting Infectiousness for Proactive Contact Tracing [75.62186539860787]
Large-scale digital contact tracing is a potential solution to resume economic and social activity while minimizing spread of the virus.
Various DCT methods have been proposed, each making trade-offs between privacy, mobility restrictions, and public health.
This paper develops and test methods that can be deployed to a smartphone to proactively predict an individual's infectiousness.
arXiv Detail & Related papers (2020-10-23T17:06:07Z) - A Deep Q-learning/genetic Algorithms Based Novel Methodology For
Optimizing Covid-19 Pandemic Government Actions [63.669642197519934]
We use the SEIR epidemiological model to represent the evolution of the virus COVID-19 over time in the population.
The sequences of actions (confinement, self-isolation, two-meter distance or not taking restrictions) are evaluated according to a reward system.
We prove that our methodology is a valid tool to discover actions governments can take to reduce the negative effects of a pandemic in both senses.
arXiv Detail & Related papers (2020-05-15T17:17:45Z)
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.