ArticleiScience2024
Deep learning model to discriminate diverse infection types based on pairwise analysis of host gene expression.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Large-scale transcriptome analysis reveals KIF20A as a novel indicator of cisplatin resistance in high-grade serous ovarian cancer.Journal of ovarian research · 2026Article
- Spatial transcriptomics identifies novel Pseudomonas aeruginosa virulence factors.Cell genomics · 2025Article
- Less is more: relative rank is more informative than absolute abundance for compositional NGS data.Briefings in functional genomics · 2025Review
- Machine learning tools for deciphering the regulatory logic of enhancers in health and disease.Frontiers in genetics · 2025Review
- scMMAE: masked cross-attention network for single-cell multimodal omics fusion to enhance unimodal omics.Briefings in bioinformatics · 2024Article
- Pairwise analysis of gene expression for oral squamous cell carcinoma via a large-scale transcriptome integration.Journal of cellular and molecular medicine · 2024Article
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Authors and funding
11 authors.
Funding
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Abstract
Accurate detection of pathogens, particularly distinguishing between Gram-positive and Gram-negative bacteria, could improve disease treatment. Host gene expression can capture the immune system's response to infections caused by various pathogens. Here, we present a deep neural network model, bvnGPS2, which incorporates the attention mechanism based on a large-scale integrated host transcriptome dataset to precisely identify Gram-positive and Gram-negative bacterial infections as well as viral infections. We performed analysis of 4,949 blood samples across 40 cohorts from 10 countries using our previously designed omics data integration method, iPAGE, to select discriminant gene pairs and train the bvnGPS2. The performance of the model was evaluated on six independent cohorts comprising 374 samples. Overall, our deep neural network model shows robust capability to accurately identify specific infections, paving the way for precise medicine strategies in infection treatment and potentially also for identifying subtypes of other diseases.
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Registered trials
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