Evidence map›Paper›PMID 30723470›Full record

ReviewFrontiers in immunology2019

The Immune System Computes the State of the Body: Crowd Wisdom, Machine Learning, and Immune Cell Reference Repertoires Help Manage Inflammation.

Irun R Cohen, Sol Efroni

Abstract readReview
In one paragraph

Review in Frontiers in immunology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed.

  1. Article
  2. Deep learning health space model for ordered responses.BMC medical informatics and decision making · 2025
    Article
  3. Review
  4. Article
  5. The Impact ofPathogens (Basel, Switzerland) · 2024
    Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Review
  12. Review
  13. Article
  14. Virus-Induced Membrane Fusion in Neurodegenerative Disorders.Frontiers in cellular and infection microbiology · 2022
    Review
  15. Review
  16. Struggle within: evolution and ecology of somatic cell populations.Cellular and molecular life sciences : CMLS · 2021
    Review
  17. Review
  18. Article
  19. Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Irun R CohenDepartment of Immunology, Weizmann Institute of Science, Rehovot, Israel.
Sol EfroniFaculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Here, we outline an overview of the mammalian immune system that updates and extends the classical clonal selection paradigm. Rather than focusing on strict self-not-self discrimination, we propose that the system orchestrates variable inflammatory responses that maintain the body and its symbiosis with the microbiome while eliminating the threat from pathogenic infectious agents and from tumors. The paper makes four points: The immune system classifies healthy and pathologic states of the body-including both self and foreign elements-by deploying individual lymphocytes as cellular computing machines; immune cells transform input signals from the body into an output of specific immune reactions.Rather than independent clonal responses, groups of individually activated immune-system cells co-react in lymphoid organs to make collective decisions through a type of self-organizing swarm intelligence or crowd wisdom.Collective choices by swarms of immune cells, like those of schools of fish, are modified by relatively small numbers of individual regulators responding to shifting conditions-such collective inflammatory responses are dynamically responsive.Self-reactive autoantibody and T-cell receptor (TCR) repertoires shared by healthy individuals function in a biological version of experience-based supervised machine learning. Immune system decisions are primed by formative experience with training sets of self-antigens encountered during lymphocyte development; these initially trained T cell and B cell repertoires form a Wellness Profile that then guides immune responses to test sets of antigens encountered later. This experience-based machine learning strategy is analogous to that deployed by supervised machine-learning algorithms. We propose experiments to test these ideas. This overview of the immune system bears clinical implications for monitoring wellness and for treating autoimmune disease, cancer, and allograft reactions.

Indexed as

Immune System PhenomenaImmunomodulationModels, BiologicalAnimalsDisease SusceptibilityHomeostasisHumansImmune SystemImmunity, CellularImmunity, HumoralImmunologic MemoryInflammationautoantibodiesautoreactive repertoiresimmune computationmachine learningswarm intelligenceT cells

Identifiers

PMID30723470
PMCPMC6349705

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.