ArticleInfectious diseases of poverty2020
Using the spike protein feature to predict infection risk and monitor the evolutionary dynamic of coronavirus.
Article in Infectious diseases of poverty, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
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Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- How do we share data in COVID-19 research? A systematic review of COVID-19 datasets in PubMed Central Articles.Briefings in bioinformatics · 2021Pooled it
- Asymptomatic SARS-CoV-2 Carriers: A Systematic Review and Meta-Analysis.Frontiers in public healthPooled it
- Identifying host-specific patterns in viral protein sequences to predict host spillover risk in animal and plant kingdoms.Scientific reports · 2026Article
- Zoon0PredV: Potential Virus Species Crossover Prediction Using Convolutional Neural Networks and Viral Protein Sequence Patterns.Bioinformatics and biology insights · 2026Article
- Article
- SARS-CoV-2 spike gene Sanger sequencing methodology to identify variants of concern.BioTechniques · 2023Article
- Prediction of coronavirus 3C-like protease cleavage sites using machine-learning algorithms.Virologica Sinica · 2022Article
- Classifying COVID-19 based on amino acids encoding with machine learning algorithms.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2022Article
- Using amino acid features to identify the pathogenicity of influenza B virus.Infectious diseases of poverty · 2022Article
- Tracking the amino acid changes of spike proteins across diverse host species of severe acute respiratory syndrome coronavirus 2.iScience · 2022Article
- SARS-CoV-2 and Emerging Variants: Unmasking Structure, Function, Infection, and Immune Escape Mechanisms.Frontiers in cellular and infection microbiology · 2022Review
- Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review.Expert systems with applications · 2021Review
- A Global Mutational Profile of SARS-CoV-2: A Systematic Review and Meta-Analysis of 368,316 COVID-19 Patients.Life (Basel, Switzerland) · 2021Review
- Prediction of pandemic risk for animal-origin coronavirus using a deep learning method.Infectious diseases of poverty · 2021Article
- The impact of the hypoxia-VEGF-vascular permeability on COVID-19-infected patients.Exploration (Beijing, China) · 2021Article
- Computational predictions for protein sequences of COVID-19 virus via machine learning algorithms.Medical & biological engineering & computing · 2021Article
- Comparative Genomics Reveals Early Emergence and Biased Spatiotemporal Distribution of SARS-CoV-2.Molecular biology and evolution · 2021Article
- Predicting the animal hosts of coronaviruses from compositional biases of spike protein and whole genome sequences through machine learning.PLoS pathogens · 2021Article
- Digital Health Solutions to Control the COVID-19 Pandemic in Countries With High Disease Prevalence: Literature Review.Journal of medical Internet research · 2021Review
- Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review.JMIR medical informatics · 2021Review
Corrections and comments
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5 authors.
Funding
Abstract
backgroundCoronavirus can cross the species barrier and infect humans with a severe respiratory syndrome. SARS-CoV-2 with potential origin of bat is still circulating in China. In this study, a prediction model is proposed to evaluate the infection risk of non-human-origin coronavirus for early warning.
methodsThe spike protein sequences of 2666 coronaviruses were collected from 2019 Novel Coronavirus Resource (2019nCoVR) Database of China National Genomics Data Center on Jan 29, 2020. A total of 507 human-origin viruses were regarded as positive samples, whereas 2159 non-human-origin viruses were regarded as negative. To capture the key information of the spike protein, three feature encoding algorithms (amino acid composition, AAC; parallel correlation-based pseudo-amino-acid composition, PC-PseAAC and G-gap dipeptide composition, GGAP) were used to train 41 random forest models. The optimal feature with the best performance was identified by the multidimensional scaling method, which was used to explore the pattern of human coronavirus.
resultsThe 10-fold cross-validation results showed that well performance was achieved with the use of the GGAP (g = 3) feature. The predictive model achieved the maximum ACC of 98.18% coupled with the Matthews correlation coefficient (MCC) of 0.9638. Seven clusters for human coronaviruses (229E, NL63, OC43, HKU1, MERS-CoV, SARS-CoV, and SARS-CoV-2) were found. The cluster for SARS-CoV-2 was very close to that for SARS-CoV, which suggests that both of viruses have the same human receptor (angiotensin converting enzyme II). The big gap in the distance curve suggests that the origin of SARS-CoV-2 is not clear and further surveillance in the field should be made continuously. The smooth distance curve for SARS-CoV suggests that its close relatives still exist in nature and public health is challenged as usual.
conclusionsThe optimal feature (GGAP, g = 3) performed well in terms of predicting infection risk and could be used to explore the evolutionary dynamic in a simple, fast and large-scale manner. The study may be beneficial for the surveillance of the genome mutation of coronavirus in the field.
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