ArticleFrontiers in microbiology2023
Genomic representation predicts an asymptotic host adaptation of bat coronaviruses using deep learning.
Article in Frontiers in microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Deep learning predicts potential reassortments of avian H5N1 with human influenza viruses.National science review · 2025Article
- Machine learning methods for predicting human-adaptive influenza A virus reassortment based on intersegment constraint.Frontiers in microbiology · 2025Article
- Integrated machine learning to predict the prognosis of lung adenocarcinoma patients based on SARS-COV-2 and lung adenocarcinoma crosstalk genes.Cancer science · 2025Article
- Compositional features analysis by machine learning in genome represents linear adaptation of monkeypox virus.Frontiers in genetics · 2024Article
- Genome composition-based deep learning predicts oncogenic potential of HPVs.Frontiers in cellular and infection microbiology · 2024Article
- Dinucleotide composition representation -based deep learning to predict scoliosis-associated Fibrillin-1 genotypes.Frontiers in genetics · 2024Article
- Risk Assessment of the Possible Intermediate Host Role of Pigs for Coronaviruses with a Deep Learning Predictor.Viruses · 2023Article
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Authors and funding
13 authors.
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Abstract
Introduction: Coronaviruses (CoVs) are naturally found in bats and can occasionally cause infection and transmission in humans and other mammals. Our study aimed to build a deep learning (DL) method to predict the adaptation of bat CoVs to other mammals. Methods: The CoV genome was represented with a method of dinucleotide composition representation (DCR) for the two main viral genes, Results and discussion: The results demonstrated inter-host separation and intra-host clustering of DCR-represented CoVs for six host types: Artiodactyla, Carnivora, Chiroptera, Primates, Rodentia/Lagomorpha, and Suiformes. The DCR-based CNN with five host labels (without Chiroptera) predicted a dominant adaptation of bat CoVs to Artiodactyla hosts, then to Carnivora and Rodentia/Lagomorpha mammals, and later to primates. Moreover, a linear asymptotic adaptation of all CoVs (except Suiformes) from Artiodactyla to Carnivora and Rodentia/Lagomorpha and then to Primates indicates an asymptotic bats-other mammals-human adaptation. Conclusion: Genomic dinucleotides represented as DCR indicate a host-specific separation, and clustering predicts a linear asymptotic adaptation shift of bat CoVs from other mammals to humans via deep learning.
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