ArticleApplied intelligence (Dordrecht, Netherlands)2021
Using artificial intelligence techniques for COVID-19 genome analysis.
Article in Applied intelligence (Dordrecht, Netherlands), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- GenoDense-Net: unraveling the genomic puzzle of the global pathogen.Tropical diseases, travel medicine and vaccines · 2025Article
- The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool.AI and ethics · 2025Article
- Paying attention to the SARS-CoV-2 dialect : a deep neural network approach to predicting novel protein mutations.Communications biology · 2025Article
- FlightTrackAI: a robust convolutional neural network-based tool for tracking the flight behaviour ofRoyal Society open science · 2024Article
- Identification and classification of coronavirus genomic signals based on linear predictive coding and machine learning methods.Biomedical signal processing and control · 2023Article
- COVID-19 in a Pre-Omicron Era: A Cross-Sectional Immuno-Epidemical and Genomic Evaluation.Vaccines · 2023Article
- Flight behaviour monitoring and quantification of aedes aegypti using convolution neural network.PloS one · 2023Article
- Article
- Prediction of Recurrent Mutations in SARS-CoV-2 Using Artificial Neural Networks.International journal of molecular sciences · 2022Article
- Heuristic Analysis of Genomic Sequence Processing Models for High Efficiency Prediction: A Statistical Perspective.Current genomics · 2022Review
- A novel logical model of COVID-19 intracellular infection to support therapies development.PLoS computational biology · 2022Article
- Enabling Artificial Intelligence for Genome Sequence Analysis of COVID-19 and Alike Viruses.Interdisciplinary sciences, computational life sciences · 2022Article
- NetNMSP: Nonoverlapping maximal sequential pattern mining.Applied intelligence (Dordrecht, Netherlands) · 2022Article
- Cross-sectional analysis and data-driven forecasting of confirmed COVID-19 cases.Applied intelligence (Dordrecht, Netherlands) · 2022Article
- Differential evolution and particle swarm optimization against COVID-19.Artificial intelligence review · 2022Article
- Connections between Various Disorders: Combination Pattern Mining Using Apriori Algorithm Based on Diagnosis Information from Electronic Medical Records.BioMed research international · 2022Article
- COVID-19 prediction based on genome similarity of human SARS-CoV-2 and bat SARS-CoV-like coronavirus.Computers & industrial engineering · 2021Article
- The prospective of Artificial Intelligence in COVID-19 Pandemic.Health and technology · 2021Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The genome of the novel coronavirus (COVID-19) disease was first sequenced in January 2020, approximately a month after its emergence in Wuhan, capital of Hubei province, China. COVID-19 genome sequencing is critical to understanding the virus behavior, its origin, how fast it mutates, and for the development of drugs/vaccines and effective preventive strategies. This paper investigates the use of artificial intelligence techniques to learn interesting information from COVID-19 genome sequences. Sequential pattern mining (SPM) is first applied on a computer-understandable corpus of COVID-19 genome sequences to see if interesting hidden patterns can be found, which reveal frequent patterns of nucleotide bases and their relationships with each other. Second, sequence prediction models are applied to the corpus to evaluate if nucleotide base(s) can be predicted from previous ones. Third, for mutation analysis in genome sequences, an algorithm is designed to find the locations in the genome sequences where the nucleotide bases are changed and to calculate the mutation rate. Obtained results suggest that SPM and mutation analysis techniques can reveal interesting information and patterns in COVID-19 genome sequences to examine the evolution and variations in COVID-19 strains respectively.
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Registered trials
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