ArticleBriefings in bioinformatics2022
Deep learning based on biologically interpretable genome representation predicts two types of human adaptation of SARS-CoV-2 variants.
Article in Briefings in bioinformatics, 2022. 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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18 citing papers in PubMed.
- In Silico Saturation-Mutagenesis-Based Genomic Mutation Risk Assessment for Enterovirus B.Viruses · 2026Article
- Genomic similarity to quantitatively evaluate the reassortment potential of H7N9 with other subtypes of avian influenza viruses.Frontiers in cellular and infection microbiology · 2026Article
- Deep learning predicts potential reassortments of avian H5N1 with human influenza viruses.National science review · 2025Article
- General Intelligence Framework to Predict Virus Adaptation Based on a Genome Language Model.Research (Washington, D.C.) · 2025Article
- Enhanced deep Convolutional Neural Network for SARS-CoV-2 variants classification.Frontiers in artificial intelligence · 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
- High-affinity optimization potential of the virus neutralizing antibody with twin cysteine-stabilized complementarity-determining region 3.Frontiers in cellular and infection microbiology · 2025Article
- A predictive language model for SARS-CoV-2 evolution.Signal transduction and targeted therapy · 2024Article
- Machine Learning Early Detection of SARS-CoV-2 High-Risk Variants.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Screening for anti-influenza virus compounds from traditional Mongolian medicine by GFP-based reporter virus.Frontiers in cellular and infection microbiology · 2024Article
- Compositional features analysis by machine learning in genome represents linear adaptation of monkeypox virus.Frontiers in genetics · 2024Article
- Editorial: Exploring genetic characteristics and molecular mechanisms of host adaptation of viruses with artificial intelligence (AI) or (and) biological (BIO) approaches.Frontiers in cellular and infection microbiology · 2024Article
- Dinucleotide composition representation -based deep learning to predict scoliosis-associated Fibrillin-1 genotypes.Frontiers in genetics · 2024Article
- Genome composition-based deep learning predicts oncogenic potential of HPVs.Frontiers in cellular and infection microbiology · 2024Article
- Risk Assessment of the Possible Intermediate Host Role of Pigs for Coronaviruses with a Deep Learning Predictor.Viruses · 2023Article
- Reversal of the unique Q493R mutation increases the affinity of Omicron S1-RBD for ACE2.Computational and structural biotechnology journal · 2023Article
- Genomic representation predicts an asymptotic host adaptation of bat coronaviruses using deep learning.Frontiers in microbiology · 2023Article
- Convolutional Neural Networks Based on Sequential Spike Predict the High Human Adaptation of SARS-CoV-2 Omicron Variants.Viruses · 2022Article
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5 authors.
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Abstract
Explosively emerging SARS-CoV-2 variants challenge current nomenclature schemes based on genetic diversity and biological significance. Genomic composition-based machine learning methods have recently performed well in identifying phenotype-genotype relationships. We introduced a framework involving dinucleotide (DNT) composition representation (DCR) to parse the general human adaptation of RNA viruses and applied a three-dimensional convolutional neural network (3D CNN) analysis to learn the human adaptation of other existing coronaviruses (CoVs) and predict the adaptation of SARS-CoV-2 variants of concern (VOCs). A markedly separable, linear DCR distribution was observed in two major genes-receptor-binding glycoprotein and RNA-dependent RNA polymerase (RdRp)-of six families of single-stranded (ssRNA) viruses. Additionally, there was a general host-specific distribution of both the spike proteins and RdRps of CoVs. The 3D CNN based on spike DCR predicted a dominant type II adaptation of most Beta, Delta and Omicron VOCs, with high transmissibility and low pathogenicity. Type I adaptation with opposite transmissibility and pathogenicity was predicted for SARS-CoV-2 Alpha VOCs (77%) and Kappa variants of interest (58%). The identified adaptive determinants included D1118H and A570D mutations and local DNTs. Thus, the 3D CNN model based on DCR features predicts SARS-CoV-2, a major type II human adaptation and is qualified to predict variant adaptation in real time, facilitating the risk-assessment of emerging SARS-CoV-2 variants and COVID-19 control.
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