Evidence map›Paper›PMID 38888783›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2024

In Silico Tools for Predicting Novel Epitopes.

Carolina Barra, Jonas Birkelund Nilsson, Astrid Saksager, Ibel Carri, Sebastian Deleuran, Heli M Garcia Alvarez, Magnus Haraldson Høie, Yuchen Li, Joakim Nøddeskov Clifford, Yat-Tsai Richie Wan and 2 more

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Carolina BarraSection for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark. carolet@dtu.dk.
Jonas Birkelund NilssonSection for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark.
Astrid SaksagerSection for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark.
Ibel CarriInstituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín (UNSAM) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), San Martín, Argentina.
Sebastian DeleuranSection for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark.
Heli M Garcia AlvarezInstituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín (UNSAM) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), San Martín, Argentina.
Magnus Haraldson HøieSection for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark.
Yuchen LiSection for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark.
Joakim Nøddeskov CliffordSection for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark.
Yat-Tsai Richie WanSection for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark.
Lys Sanz MoretaSection for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark.
Morten NielsenSection for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identifying antigens within a pathogen is a critical task to develop effective vaccines and diagnostic methods, as well as understanding the evolution and adaptation to host immune responses. Historically, antigenicity was studied with experiments that evaluate the immune response against selected fragments of pathogens. Using this approach, the scientific community has gathered abundant information regarding which pathogenic fragments are immunogenic. The systematic collection of this data has enabled unraveling many of the fundamental rules underlying the properties defining epitopes and immunogenicity, and has resulted in the creation of a large panel of immunologically relevant predictive (in silico) tools. The development and application of such tools have proven to accelerate the identification of novel epitopes within biomedical applications reducing experimental costs. This chapter introduces some basic concepts about MHC presentation, T cell and B cell epitopes, the experimental efforts to determine those, and focuses on state-of-the-art methods for epitope prediction, highlighting their strengths and limitations, and catering instructions for their rational use.

Indexed as

Computational BiologyComputer SimulationEpitopes, B-LymphocyteEpitopes, T-LymphocyteAnimalsAntigen PresentationEpitope MappingEpitopesHumansSoftwareEpitopesEpitopes, B-LymphocyteEpitopes, T-LymphocyteB cell epitopesEpitope predictionImmunogenicity predictionImmunogenicity predictionMHC presentationT cell epitopes

Identifiers

What OpenQuestion holds

Textmetadata
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.