ArticleBriefings in bioinformatics2024
PRIEST: predicting viral mutations with immune escape capability of SARS-CoV-2 using temporal evolutionary information.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Viral escape-inspired framework for structure-guided dual bait protein biosensor design.PLoS computational biology · 2025Article
- Predicting pathogen evolution and immune evasion in the age of artificial intelligence.Computational and structural biotechnology journal · 2025Review
- Exploring the ability of the MD+FoldX method to predict SARS-CoV-2 antibody escape mutations using large-scale data.Scientific reports · 2024Article
- Exploring the ability of the MD+FoldX method to predict SARS-CoV-2 antibody escape mutations using large-scale data.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The dynamic evolution of the severe acute respiratory syndrome coronavirus 2 virus is primarily driven by mutations in its genetic sequence, culminating in the emergence of variants with increased capability to evade host immune responses. Accurate prediction of such mutations is fundamental in mitigating pandemic spread and developing effective control measures. This study introduces a robust and interpretable deep-learning approach called PRIEST. This innovative model leverages time-series viral sequences to foresee potential viral mutations. Our comprehensive experimental evaluations underscore PRIEST's proficiency in accurately predicting immune-evading mutations. Our work represents a substantial step in utilizing deep-learning methodologies for anticipatory viral mutation analysis and pandemic response.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.