ArticleLife (Basel, Switzerland)2025
Transcriptomic and miRNA Signatures of ChAdOx1 nCoV-19 Vaccine Response Using Machine Learning.
Article in Life (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
Abstract
Vaccination with ChAdOx1 nCoV-19 is an important countermeasure to fight the COVID-19 pandemic. This vaccine enhances human immunoprotection against SARS-CoV-2 by inducing an immune response against the SARS-CoV-2 S protein. However, the immune-related genes induced by vaccination remain to be identified. This study employs feature ranking algorithms, an incremental feature selection method, and classification algorithms to analyze transcriptomic data from an experimental group vaccinated with the ChAdOx1 nCoV-19 vaccine and a control group vaccinated with the MenACWY meningococcal vaccine. According to different time points, vaccination status, and SARS-CoV-2 infection status, the transcriptomic data was divided into five groups, including a pre-vaccination group, ChAdOx1-onset group, MenACWY-onset group, ChAdOx1-7D group, and MenACWY-7D group. Each group contained samples with 13,383 RNA features and 1662 small RNA features. The results identified key genes that could indicate the efficacy of the ChAdOx1 nCoV-19 vaccine, and a classifier was developed to classify samples into the above groups. Additionally, effective classification rules were established to distinguish between different vaccination statuses. It was found that subjects vaccinated with ChAdOx1 nCoV-19 vaccine and infected with SARS-CoV-2 were characterized by up-regulation of HIST1H3G expression and down-regulation of CASP10 expression. In addition, IGHG1, FOXM1, and CASP10 genes were strongly associated with ChAdOx1 nCoV-19 vaccine efficacy. Compared with previous omics-driven studies, the machine learning algorithms used in this study were able to analyze transcriptome data faster and more comprehensively to identify potential markers associated with vaccine effect and investigate ChAdOx1 nCoV-19 vaccine-induced gene expression changes. These observations contribute to an understanding of the immune protection and inflammatory responses induced by the ChAdOx1 nCoV-19 vaccine during symptomatic episodes and provide a rationale for improving vaccine efficacy.
Indexed as
Identifiers
What Socratic 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 Socratic graph.