ArticleTuberculosis (Edinburgh, Scotland)2015
A tuberculosis ontology for host systems biology.
Article in Tuberculosis (Edinburgh, Scotland), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- Peripheral Blood Markers Correlate with the Progression of Active Tuberculosis Relative to Latent Control ofPathogens (Basel, Switzerland) · 2022Article
- Article
- Mucosal-activated invariant T cells do not exhibit significant lung recruitment and proliferation profiles in macaques in response to infection with Mycobacterium tuberculosis CDC1551.Tuberculosis (Edinburgh, Scotland) · 2019Article
- Nonpathologic Infection of Macaques by an Attenuated Mycobacterial Vaccine Is Not Reactivated in the Setting of HIV Co-Infection.The American journal of pathology · 2017Article
- Hypoxia Sensing and Persistence Genes Are Expressed during the Intragranulomatous Survival of Mycobacterium tuberculosis.American journal of respiratory cell and molecular biology · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
A major hurdle facing tuberculosis (TB) investigators who want to utilize a rapidly growing body of data from both systems biology approaches and omics technologies is the lack of a standard vocabulary for data annotation and reporting. Lacking a means to readily compare samples from different research groups, a significant quantity of potentially informative data is largely ignored by researchers. To facilitate standardizing data across studies, a simple ontology of TB terms was developed to provide a common vocabulary for annotating data sets. New terminology was developed to address animal models and experimental systems, and existing clinically focused terminology was modified and adapted. This ontology can be used to annotate host TB data in public databases and collaborations, thereby standardizing database searches and allowing researchers to more easily compare results. To demonstrate the utility of a standard TB ontology for host systems biology, a web application was developed to annotate and compare human and animal model gene expression data sets.
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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.