ArticleComputers in biology and medicine2023
TEMPO: A transformer-based mutation prediction framework for SARS-CoV-2 evolution.
Article in Computers in biology and medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed, 35 citations in OpenAlex.
- TEAM: A time-enhanced attention-based model for virus mutation prediction.Quantitative biology (Beijing, China) · 2026Article
- Decoding viral protein sequences by large language models.Briefings in bioinformatics · 2026Review
- A deep mutational scanning-informed protein language model predicts SARS-CoV-2 evolution dynamics with spatiotemporal resolution.Nature microbiology · 2026Article
- AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding.Archives of microbiology · 2026Review
- Machine Learning-Driven Simulations of the SARS-CoV-2 Fitness Landscape from Deep Mutational Scanning Experiments.Journal of chemical information and modeling · 2026Article
- Predicting the evolution and persistence of COVID-19 using context-dependent polymorphisms.Scientific reports · 2026Article
- Paying attention to the SARS-CoV-2 dialect : a deep neural network approach to predicting novel protein mutations.Communications biology · 2025Article
- Unveiling the ghost: machine learning's impact on the landscape of virology.The Journal of general virology · 2025Review
- Transfer learning towards predicting viral missense mutations: A case study on SARS-CoV-2.Computational and structural biotechnology journal · 2025Article
- Bag-of-words is competitive with sum-of-embeddings language-inspired representations on protein inference.PloS one · 2025Article
- Predicting pathogen evolution and immune evasion in the age of artificial intelligence.Computational and structural biotechnology journal · 2025Review
- Article
- A predictive language model for SARS-CoV-2 evolution.Signal transduction and targeted therapy · 2024Article
- A prediction of mutations in infectious viruses using artificial intelligence.Genomics & informatics · 2024Article
- DeepPL: A deep-learning-based tool for the prediction of bacteriophage lifecycle.PLoS computational biology · 2024Article
- Forecasting dominance of SARS-CoV-2 lineages by anomaly detection using deep AutoEncoders.bioRxiv : the preprint server for biology · 2024Article
- Forecasting dominance of SARS-CoV-2 lineages by anomaly detection using deep AutoEncoders.Briefings in bioinformatics · 2024Article
- PRIEST: predicting viral mutations with immune escape capability of SARS-CoV-2 using temporal evolutionary information.Briefings in bioinformatics · 2024Article
- Development of a two-component recombinant vaccine for COVID-19.Frontiers in immunology · 2024Article
- Variation and evolution analysis of SARS-CoV-2 using self-game sequence optimization.Frontiers in microbiology · 2024Article
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Authors and funding
8 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The widespread of SARS-CoV-2 presents a significant threat to human society, as well as public health and economic development. Extensive efforts have been undertaken to battle against the pandemic, whereas effective approaches such as vaccination would be weakened by the continuous mutations, leading to considerable attention being attracted to the mutation prediction. However, most previous studies lack attention to phylogenetics. In this paper, we propose a novel and effective model TEMPO for predicting the mutation of SARS-CoV-2 evolution. Specifically, we design a phylogenetic tree-based sampling method to generate sequence evolution data. Then, a transformer-based model is presented for the site mutation prediction after learning the high-level representation of these sequence data. We conduct experiments to verify the effectiveness of TEMPO, leveraging a large-scale SARS-CoV- 2 dataset. Experimental results show that TEMPO is effective for mutation prediction of SARS- CoV-2 evolution and outperforms several state-of-the-art baseline methods. We further perform mutation prediction experiments of other infectious viruses, to explore the feasibility and robustness of TEMPO, and experimental results verify its superiority. The codes and datasets are freely available at https://github.com/ZJUDataIntelligence/TEMPO.
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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.