Evidence map›Paper›PMID 38664818›Full record

ArticleJournal of biomedical semantics2024

Ontological representation, modeling, and analysis of parasite vaccines.

Anthony Huffman, Xumeng Zhang, Meghana Lanka, Jie Zheng, Anna Maria Masci, Yongqun He

Open access · goldAbstract read
In one paragraph

Article in Journal of biomedical semantics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.8field-weighted citation impact, top 28% 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

5 citing papers in PubMed, 2 citations in OpenAlex.

  1. VO: The Vaccine Ontology.Scientific data · 2026
    Article
  2. Review
  3. VO: The Vaccine Ontology.bioRxiv : the preprint server for biology · 2025
    Article
  4. A fourfold pathogen reference ontology suite.Journal of biomedical semantics · 2025
    Article
  5. 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

6 authors at 3 institutions in 1 country.

Anthony Huffman *Department of Computational Medicine and Bioinformatics, University of Michigan Medicine School, 48109, Ann Arbor, MI, USA.
Xumeng Zhang *College of Literature, Science, and the Arts, University of Michigan, 48109, Ann Arbor, MI, USA.
Meghana LankaCollege of Literature, Science, and the Arts, University of Michigan, 48109, Ann Arbor, MI, USA.
Jie ZhengUnit for Laboratory Animal Medicine, University of Michigan Medicine School, 48109, Ann Arbor, MI, USA.
Anna Maria MasciDepartment of Data Impact and Governance, MD Anderson Cancer Center University of Texas, TX, 77030, Houston, USA.
Yongqun HeDepartment of Computational Medicine and Bioinformatics, University of Michigan Medicine School, 48109, Ann Arbor, MI, USA. yongqunh@med.umich.edu.
University of Michigan–Ann Arbor · USMichigan Medicine · USThe University of Texas MD Anderson Cancer Center · US

Funding

VIOLIN 2.0: Vaccine Information and Ontology LInked kNowledgebaseU24AI171008 · NIAID · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Yongqun He, Junguk Hur · 2022 to 2026
$4.2M
NIAID NIH HHS U24 AI171008NIH-NIAID U24A171008
6 · The paper itself

Abstract

backgroundPathogenic parasites are responsible for multiple diseases, such as malaria and Chagas disease, in humans and livestock. Traditionally, pathogenic parasites have been largely an evasive topic for vaccine design, with most successful vaccines only emerging recently. To aid vaccine design, the VIOLIN vaccine knowledgebase has collected vaccines from all sources to serve as a comprehensive vaccine knowledgebase. VIOLIN utilizes the Vaccine Ontology (VO) to standardize the modeling of vaccine data. VO did not model complex life cycles as seen in parasites. With the inclusion of successful parasite vaccines, an update in parasite vaccine modeling was needed.

resultsVIOLIN was expanded to include 258 parasite vaccines against 23 protozoan species, and 607 new parasite vaccine-related terms were added to VO since 2022. The updated VO design for parasite vaccines accounts for parasite life stages and for transmission-blocking vaccines. A total of 356 terms from the Ontology of Parasite Lifecycle (OPL) were imported to VO to help represent the effect of different parasite life stages. A new VO class term, 'transmission-blocking vaccine,' was added to represent vaccines able to block infectious transmission, and one new VO object property, 'blocks transmission of pathogen via vaccine,' was added to link vaccine and pathogen in which the vaccine blocks the transmission of the pathogen. Additionally, our Gene Set Enrichment Analysis (GSEA) of 140 parasite antigens used in the parasitic vaccines identified enriched features. For example, significant patterns, such as signal, plasma membrane, and entry into host, were found in the antigens of the vaccines against two parasite species: Plasmodium falciparum and Toxoplasma gondii. The analysis found 18 out of the 140 parasite antigens involved with the malaria disease process. Moreover, a majority (15 out of 54) of P. falciparum parasite antigens are localized in the cell membrane. T. gondii antigens, in contrast, have a majority (19/24) of their proteins related to signaling pathways. The antigen-enriched patterns align with the life cycle stage patterns identified in our ontological parasite vaccine modeling.

conclusionsThe updated VO modeling and GSEA analysis capture the influence of the complex parasite life cycles and their associated antigens on vaccine development.

Indexed as

Biological OntologiesAnimalsHumansModels, BiologicalParasitesProtozoan VaccinesVaccinesProtozoan VaccinesVaccinesParasite vaccineVaccine OntologyVIOLIN vaccine knowledgebase

Identifiers

PMID38664818
PMCPMC11044459
OpenAlexW4395463966

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.