ArticleEURASIP journal on bioinformatics & systems biology2016
The prediction of virus mutation using neural networks and rough set techniques.
Article in EURASIP journal on bioinformatics & systems biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Viral escape-inspired framework for structure-guided dual bait protein biosensor design.PLoS computational biology · 2025Article
- Unsupervised attribute reduction based on variable precision weighted neighborhood dependency.iScience · 2024Article
- PRIEST: predicting viral mutations with immune escape capability of SARS-CoV-2 using temporal evolutionary information.Briefings in bioinformatics · 2024Article
- An ensemble-based deep learning model for detection of mutation causing cutaneous melanoma.Scientific reports · 2023Article
- Prediction of Recurrent Mutations in SARS-CoV-2 Using Artificial Neural Networks.International journal of molecular sciences · 2022Article
- The Advantage of Using Immunoinformatic Tools on Vaccine Design and Development for Coronavirus.Vaccines · 2022Review
- Application of machine learning in understanding plant virus pathogenesis: trends and perspectives on emergence, diagnosis, host-virus interplay and management.Virology journal · 2022Review
- Possibility of estimating future mutants for influenza: Comparison between previous prediction and subsequent years observation.Frontiers in microbiology · 2022Article
- How artificial intelligence may help the Covid-19 pandemic: Pitfalls and lessons for the future.Reviews in medical virology · 2021Review
- Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19.Pathogens (Basel, Switzerland) · 2021Review
- IoT Platform for COVID-19 Prevention and Control: A Survey.IEEE access : practical innovations, open solutions · 2021Article
- Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review.Journal of medical Internet research · 2020Article
- Emergence of New Disease: How Can Artificial Intelligence Help?Trends in molecular medicine · 2020Review
- Identification of Proteins of Tobacco Mosaic Virus by Using a Method of Feature Extraction.Frontiers in genetics · 2020Article
- Machine-learning based patient classification using Hepatitis B virus full-length genome quasispecies from Asian and European cohorts.Scientific reports · 2019Article
- Review
- Epidemiological challenges in pandemic coronavirus disease (COVID-19): Role of artificial intelligence.Wiley interdisciplinary reviews. Data mining and knowledge discoveryReview
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Viral evolution remains to be a main obstacle in the effectiveness of antiviral treatments. The ability to predict this evolution will help in the early detection of drug-resistant strains and will potentially facilitate the design of more efficient antiviral treatments. Various tools has been utilized in genome studies to achieve this goal. One of these tools is machine learning, which facilitates the study of structure-activity relationships, secondary and tertiary structure evolution prediction, and sequence error correction. This work proposes a novel machine learning technique for the prediction of the possible point mutations that appear on alignments of primary RNA sequence structure. It predicts the genotype of each nucleotide in the RNA sequence, and proves that a nucleotide in an RNA sequence changes based on the other nucleotides in the sequence. Neural networks technique is utilized in order to predict new strains, then a rough set theory based algorithm is introduced to extract these point mutation patterns. This algorithm is applied on a number of aligned RNA isolates time-series species of the Newcastle virus. Two different data sets from two sources are used in the validation of these techniques. The results show that the accuracy of this technique in predicting the nucleotides in the new generation is as high as 75 %. The mutation rules are visualized for the analysis of the correlation between different nucleotides in the same RNA sequence.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.