ReviewAnnual review of immunology2018
Systems Immunology: Learning the Rules of the Immune System.
Review in Annual review of immunology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 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
48 citing papers in PubMed.
- A unified network systems approach uncovers a core program underlying T follicular helper cell differentiation.Nature communications · 2026Article
- Review
- From empirical vaccinology to predictive systems-based vaccine design: multi-omics integration, artificial intelligence, and global equity challenges.Frontiers in systems biology · 2026Review
- The exploration of immune system function changes in marathon athletes after high-intensity training by Agent-Based Model.Frontiers in sports and active living · 2026Article
- Context-dependent immune regulation: a mechanistic and AI-enabled integrative framework.Frontiers in immunology · 2026Review
- Cross-species transcriptomics translation reveals a role for the unfolded protein response in Mycobacterium tuberculosis infection.NPJ systems biology and applications · 2025Article
- Artificial intelligence and machine learning in the development of vaccines and immunotherapeutics-yesterday, today, and tomorrow.Frontiers in artificial intelligence · 2025Review
- Nanomaterial-Driven Precision Immunomodulation: A New Paradigm in Therapeutic Interventions.Cancers · 2024Review
- Unravelling the impact of SARS-CoV-2 on hemostatic and complement systems: a systems immunology perspective.Frontiers in immunology · 2024Article
- Higher immune-related gene expression in major depression is independent of CRP levels: results from the BIODEP study.Translational psychiatry · 2023Article
- Metabolomics meets systems immunology.EMBO reports · 2023Review
- Potential Active Marine Peptides as Anti-Aging Drugs or Drug Candidates.Marine drugs · 2023Review
- IEDB and CEDAR: Two Sibling Databases to Serve the Global Scientific Community.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Essential Regression: A generalizable framework for inferring causal latent factors from multi-omic datasets.Patterns (New York, N.Y.) · 2022Article
- Purinergic modulation of the immune response to infections.Purinergic signalling · 2022Review
- Systems-based approaches to study immunometabolism.Cellular & molecular immunology · 2022Review
- Understanding the constitutive presentation of MHC class I immunopeptidomes in primary tissues.iScience · 2022Article
- Systems neuroimmunology: a review of multiomics methodologies to characterize neuroimmunological interactions in spinal and cranial diseases.Neurosurgical focus · 2022Review
- Vesicular traffic-mediated cell-to-cell signaling at the immune synapse in Ankylosing Spondylitis.Frontiers in immunology · 2022Review
- CAD v1.0: Cancer Antigens Database Platform for Cancer Antigen Algorithm Development and Information Exploration.Frontiers in bioengineering and biotechnology · 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
3 authors.
Funding
Abstract
Given the many cell types and molecular components of the human immune system, along with vast variations across individuals, how should we go about developing causal and predictive explanations of immunity? A central strategy in human studies is to leverage natural variation to find relationships among variables, including DNA variants, epigenetic states, immune phenotypes, clinical descriptors, and others. Here, we focus on how natural variation is used to find patterns, infer principles, and develop predictive models for two areas: (a) immune cell activation-how single-cell profiling boosts our ability to discover immune cell types and states-and (b) antigen presentation and recognition-how models can be generated to predict presentation of antigens on MHC molecules and their detection by T cell receptors. These are two examples of a shift in how we find the drivers and targets of immunity, especially in the human system in the context of health and disease.
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Identifiers
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.