Evidence map›Paper›PMID 34603286›Full record

ReviewFrontiers in immunology2021

T Cell Epitope Prediction and Its Application to Immunotherapy.

Anna-Lisa Schaap-Johansen, Milena Vujović, Annie Borch, Sine Reker Hadrup, Paolo Marcatili

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed
4.3field-weighted citation impact, top 4% of its field
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

50 citing papers in PubMed, 75 citations in OpenAlex.

  1. Article
  2. Therapeutic vaccines for NSCLC: From mechanisms to clinical translation.Chinese medical journal pulmonary and critical care medicine · 2026
    Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. Review
  8. Article
  9. Review
  10. Review
  11. Design of a Multi-Epitope Vaccine Based onInternational journal of molecular sciences · 2025
    Article
  12. Three-Dimensional Modeling ofAntibodies (Basel, Switzerland) · 2025
    Article
  13. Article
  14. Brain communications · 2025
    Review
  15. Article
  16. Review
  17. Article
  18. Review
  19. Article
  20. RankMHC: Learning to Rank Class-I Peptide-MHC Structural Models.Journal of chemical information and modeling · 2024
    Article
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

5 authors at 1 institution in 1 country.

Anna-Lisa Schaap-JohansenDepartment of Health Technology, Technical University of Denmark, Lyngby, Denmark.
Milena VujovićDepartment of Health Technology, Technical University of Denmark, Lyngby, Denmark.
Annie BorchDepartment of Health Technology, Technical University of Denmark, Lyngby, Denmark.
Sine Reker HadrupDepartment of Health Technology, Technical University of Denmark, Lyngby, Denmark.
Paolo MarcatiliDepartment of Health Technology, Technical University of Denmark, Lyngby, Denmark.
Technical University of Denmark · DK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

T cells play a crucial role in controlling and driving the immune response with their ability to discriminate peptides derived from healthy as well as pathogenic proteins. In this review, we focus on the currently available computational tools for epitope prediction, with a particular focus on tools aimed at identifying neoepitopes, i.e. cancer-specific peptides and their potential for use in immunotherapy for cancer treatment. This review will cover how these tools work, what kind of data they use, as well as pros and cons in their respective applications.

Indexed as

ImmunotherapyNeural Networks, ComputerAntigen PresentationAntigens, NeoplasmBase SequenceComputational BiologyEpitopes, T-LymphocyteHistocompatibility Antigens Class IHumansMass SpectrometryModels, MolecularNeoplasmsPeptidesReceptors, Antigen, T-CellSequence Analysis, DNAT-Cell Antigen Receptor SpecificityAntigens, NeoplasmEpitopes, T-LymphocyteHistocompatibility Antigens Class IPeptidesReceptors, Antigen, T-Cellepitope predictionneoantigensneoepitope predictionT cellT cell receptorTCR

Identifiers

PMID34603286
PMCPMC8479193
OpenAlexW3201078782

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.