ArticleBioMed research international2017
Computational Identification and Characterization of a Promiscuous T-Cell Epitope on the Extracellular Protein 85B of
Article in BioMed research international, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 17 citations in OpenAlex.
- The Use and Importance ofArtificial Intelligence in Vaccine Research, Development, and Production.Archives of Razi Institute · 2025Review
- Immunogenicity and Protective Efficacy of a Multi-AntigenVaccines · 2024Article
- COVID-19 vaccine design using reverse and structural vaccinology, ontology-based literature mining and machine learning.Briefings in bioinformatics · 2022Article
- Application of Reverse Vaccinology and Immunoinformatic Strategies for the Identification of Vaccine Candidates Against Shigella flexneri.Methods in molecular biology (Clifton, N.J.) · 2022Article
- An integrated computational framework to design a multi-epitopes vaccine against Mycobacterium tuberculosis.Scientific reports · 2021Article
- Vaxign2: the second generation of the first Web-based vaccine design program using reverse vaccinology and machine learning.Nucleic acids research · 2021Article
- Vaxign-ML: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens.Bioinformatics (Oxford, England) · 2020Article
- Identification of Immune Responses to Japanese Encephalitis Virus Specific T Cell Epitopes.Frontiers in public health · 2020Article
- Immunoinformatics Based Study of T Cell Epitopes in Zea m 1 Pollen Allergen.Medicina (Kaunas, Lithuania) · 2019Article
- Current progress of immunoinformatics approach harnessed for cellular- and antibody-dependent vaccine design.Pathogens and global health · 2018Article
- Dynamics of Immune Responses during ExperimentalMediators of inflammation · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tuberculosis (TB) is a reemerging disease that remains as a leading cause of morbidity and mortality in humans. To identify and characterize a T-cell epitope suitable for vaccine design, we have utilized the Vaxign server to assess all antigenic proteins of
Indexed as
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.