Identification of host gene transcripts by machine learning and their application to predict outcome in Ebola virus disease

Clicks: 1
ID: 317655
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #404 of 431 articles by views in The Journal of infectious diseases

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 431 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Background In humans, Ebola virus disease (EVD) has a high mortality rate. While viral load is a known prognostic factor, it is not effective in predicting outcomes across all patients. Exploring host transcriptomic signatures may provide additional predictive value. Methods Here, the transcript abundance of four candidate host genes (VCAM1, TUBG1, MS4A4A, ISG15) was quantified by RT-qPCR in blood samples from hospitalised patients with Ebola virus (EBOV) collected during the 2013–2016 West African outbreak. Samples were taken for diagnostic purposes during the acute phase of infection. Associations between target gene expression, viral load, and outcome (survival/death) were assessed, and machine learning models were trained and tested to predict patient outcome. Results EBOV Ct values were significantly higher in hospitalised survivors compared to fatalities. During the acute phase, the transcript abundance of VCAM1 and MS4A4A was higher in fatal cases, while TUBG1 expression was elevated in survivors; ISG15 expression did not differ by outcome. In support vector machine models, EBOV Ct alone classified patient outcomes with 86.9% accuracy, while MS4A4A alone reached 82.6%. Combining EBOV Ct with MS4A4A improved prediction to 91.3%. The best-performing model, integrating EBOV Ct, ISG15, and MS4A4A, achieved 100% accuracy in the test set. Discussion Host gene expression markers, particularly MS4A4A, in combination with viral load, improved outcome prediction in EVD beyond viral load alone inambiguous cases with mid-range Ct values. These findings highlight the utility of host transcript-based assays to inform clinical management and for benchmarking the effectiveness of medical countermeasures during outbreaks.
Reference Key
openalex_W7164998855 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jocelyn G. Perez, Natasha Y. Rickett, Stephan Günther, Romy Kerber, Lisa F P Ng, Tom Fletcher, Tracy MacGill, Todd Myers, Robert Orr, Miles W. Carroll, Neil Blake, Julian A. Hiscox
Journal The Journal of infectious diseases
Year 2026
DOI
10.1093/infdis/jiag308
URL
https://doi.org/10.1093/infdis/jiag308
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.