ArticleCell systems2024
Markov field network model of multi-modal data predicts effects of immune system perturbations on intravenous BCG vaccination in macaques.
Article in Cell systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Industrial-scale mRNA expertise meets a century of tuberculosis immunology.Nature immunology · 2026Article
- Immunological mechanistic action of intravenous BCG-mediated protection against tuberculosis.Cellular and molecular life sciences : CMLS · 2026Review
- Protein networks are influenced by maternal BMI and differentiate preterm birth types.Communications medicine · 2026Article
- The eight pillars of within-host tuberculosis modelling.Frontiers in immunology · 2026Review
- A proteome-wide atlas of humoral immunity toFrontiers in immunology · 2026Article
- The BCGMethodsX · 2025Article
- From network biology to immunity: potential longitudinal biomarkers for targeting the network topology of the HIV reservoir.Journal of translational medicine · 2025Review
- Deconvoluting the interplay of innate and adaptive immunity in BCG-induced nonspecific and TB-specific host resistance.The Journal of experimental medicine · 2025Review
- Intravenous BCG-mediated protection against tuberculosis requires CD4+ T cells and CD8α+ lymphocytes.The Journal of experimental medicine · 2025Article
- Editorial: Networks and graphs in biological data: current methods, opportunities and challenges.Frontiers in bioinformatics · 2025Article
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20 authors.
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Abstract
Analysis of multi-modal datasets can identify multi-scale interactions underlying biological systems but can be beset by spurious connections due to indirect impacts propagating through an unmapped biological network. For example, studies in macaques have shown that Bacillus Calmette-Guerin (BCG) vaccination by an intravenous route protects against tuberculosis, correlating with changes across various immune data modes. To eliminate spurious correlations and identify critical immune interactions in a public multi-modal dataset (systems serology, cytokines, and cytometry) of vaccinated macaques, we applied Markov fields (MFs), a data-driven approach that explains vaccine efficacy and immune correlations via multivariate network paths, without requiring large numbers of samples (i.e., macaques) relative to multivariate features. We find that integrating multiple data modes with MFs helps remove spurious connections. Finally, we used the MF to predict outcomes of perturbations at various immune nodes, including an experimentally validated B cell depletion that induced network-wide shifts without reducing vaccine protection.
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