ArticleScientific data2019
PathoPhenoDB, linking human pathogens to their phenotypes in support of infectious disease research.
Article in Scientific data, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Benchmarking large language models for pathogen-disease classification in post-acute infection syndromes.Briefings in bioinformatics · 2026Article
- Unveiling the ghost: machine learning's impact on the landscape of virology.The Journal of general virology · 2025Review
- Ontologizing health systems data at scale: making translational discovery a reality.NPJ digital medicine · 2023Article
- Classifying literature mentions of biological pathogens as experimentally studied using natural language processing.Journal of biomedical semantics · 2023Article
- Sex differences in electrolyte imbalances caused by SARS-CoV-2: A cross-sectional study.International journal of clinical practice · 2021Article
- The Human Phenotype Ontology in 2021.Nucleic acids research · 2021Article
- Combining lexical and context features for automatic ontology extension.Journal of biomedical semantics · 2020Article
- Ontology based mining of pathogen-disease associations from literature.Journal of biomedical semantics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Understanding the relationship between the pathophysiology of infectious disease, the biology of the causative agent and the development of therapeutic and diagnostic approaches is dependent on the synthesis of a wide range of types of information. Provision of a comprehensive and integrated disease phenotype knowledgebase has the potential to provide novel and orthogonal sources of information for the understanding of infectious agent pathogenesis, and support for research on disease mechanisms. We have developed PathoPhenoDB, a database containing pathogen-to-phenotype associations. PathoPhenoDB relies on manual curation of pathogen-disease relations, on ontology-based text mining as well as manual curation to associate host disease phenotypes with infectious agents. Using Semantic Web technologies, PathoPhenoDB also links to knowledge about drug resistance mechanisms and drugs used in the treatment of infectious diseases. PathoPhenoDB is accessible at http://patho.phenomebrowser.net/ , and the data are freely available through a public SPARQL endpoint.
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.