ArticleJournal of advanced research2026
Prediction of influenza virus infection based on deep learning and peripheral blood proteomics: A diagnostic study.
Article in Journal of advanced research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- SGCRNA: spectral clustering-guided co-expression network analysis without scale-free constraints for multi-omic data.Briefings in bioinformatics · 2026Article
- Neutrophil extracellular traps and acute kidney injury: a narrative review.Frontiers in immunology · 2026Review
- Dynamic ensemble deep learning with multi-source data for robust influenza forecasting in Yangzhou.BMC public health · 2025Article
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Authors and funding
12 authors.
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Abstract
introductionInfluenza viruses cause seasonal epidemics almost every year, and it is difficult to diagnose quickly and accurately. Machine learning and peripheral blood protein omics have brought new ideas to the research of clinical markers.
objectivesPrediction of key molecular marker of influenza virus infection by the established machine learning model and peripheral blood protein omics.
methodsThis study used the testing data of 850 patients (including influenza, COVID-19 and mixed infections) and 265 healthy individuals, to establish and validate a diagnostic prediction model for influenza infection and verified the potential value of this model in the differential diagnosis of influenza, COVID-19 and healthy people.
resultsThe overall analysis showed that there were significant differences in 9 clinical features in the influenza group. Principal component analysis can effectively group samples based on these clinical features. Based on the random forest model and LASSO regression model found that the selected features are clinical indicators that can accurately distinguish influenza patients. We performed proteome sequencing combined with machine learning and found a total of 26 DEPs. Through PPI and WGCNA analysis, we identified several genes related to the proportion of monocytes. We then analyzed the correlation of these factors with immune cell proportions and found that SAA1 and SAA2 were highly correlated with various vital immunocyte. ROC curve analysis shows that SERPINA3 can distinguish influenza, COVID-19, mixed infection and healthy people; SAA1 can distinguish COVID-19, mixed infection and healthy people; SAA2 can distinguish influenza and healthy people. In influenza, high expression of SERPINA3, SAA1, and SAA2 is associated with higher risk. Finally, we used the ELISA method to confirm that SAA2 protein can be used as an auxiliary diagnostic indicator for influenza infection.
conclusionsPreliminary results showed that SAA2 is an important molecular marker specific to influenza infection.
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