ArticleNature biotechnology2018
Immune-centric network of cytokines and cells in disease context identified by computational mining of PubMed.
Article in Nature biotechnology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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
45 citing papers in PubMed.
- Network-based machine learning to identify biomarkers for systemic lupus erythematosus.BMC biology · 2026Article
- Transforming the cytokine literature into a resource for experimental analysis and discovery.bioRxiv : the preprint server for biology · 2026Article
- Natural language processing of biomedical text to map and prioritize protein-disease associations in HFpEF.Computers in biology and medicine · 2026Article
- A single-cell cytokine dictionary of human peripheral blood.bioRxiv : the preprint server for biology · 2025Article
- A single-cell cytokine dictionary of human peripheral blood.Research square · 2025Article
- Article
- ENQUIRE automatically reconstructs, expands, and drives enrichment analysis of gene and Mesh co-occurrence networks from context-specific biomedical literature.PLoS computational biology · 2025Article
- Review
- Neuroimmune crosstalk in chronic neuroinflammation: microglial interactions and immune modulation.Frontiers in cellular neuroscience · 2025Review
- Effect ofFrontiers in veterinary science · 2025Article
- Article
- A count-based model for delineating cell-cell interactions in spatial transcriptomics data.Bioinformatics (Oxford, England) · 2024Article
- Plasma and urine proteomics and gut microbiota analysis reveal potential factors affecting COVID-19 vaccination response.iScience · 2024Article
- MACC: a visual interactive knowledgebase of metabolite-associated cell communications.Nucleic acids research · 2024Article
- A narrative review on the role of cytokines in the pathogenesis and treatment of familial Mediterranean fever: an emphasis on pediatric cases.Frontiers in pediatrics · 2024Review
- Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study.Journal of medical Internet research · 2023Article
- Multi-omic longitudinal study reveals immune correlates of clinical course among hospitalized COVID-19 patients.Cell reports. Medicine · 2023Article
- Multi-omics delineation of cytokine-induced endothelial inflammatory states.Communications biology · 2023Article
- Splicing signature database development to delineate cancer pathways using literature mining and transcriptome machine learning.Computational and structural biotechnology journal · 2023Article
- Mapping the epithelial-immune cell interactome upon infection in the gut and the upper airways.NPJ systems biology and applications · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
Abstract
Cytokines are signaling molecules secreted and sensed by immune and other cell types, enabling dynamic intercellular communication. Although a vast amount of data on these interactions exists, this information is not compiled, integrated or easily searchable. Here we report immuneXpresso, a text-mining engine that structures and standardizes knowledge of immune intercellular communication. We applied immuneXpresso to PubMed to identify relationships between 340 cell types and 140 cytokines across thousands of diseases. The method is able to distinguish between incoming and outgoing interactions, and it includes the effect of the interaction and the cellular function involved. These factors are assigned a confidence score and linked to the disease. By leveraging the breadth of this network, we predicted and experimentally verified previously unappreciated cell-cytokine interactions. We also built a global immune-centric view of diseases and used it to predict cytokine-disease associations. This standardized knowledgebase (http://www.immunexpresso.org) opens up new directions for interpretation of immune data and model-driven systems immunology.
Indexed as
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What OpenQuestion 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 OpenQuestion graph.