ArticleJournal of biomedical semantics2019
Ontology based mining of pathogen-disease associations from literature.
Article in Journal of biomedical semantics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Benchmarking large language models for pathogen-disease classification in post-acute infection syndromes.Briefings in bioinformatics · 2026Article
- Classifying literature mentions of biological pathogens as experimentally studied using natural language processing.Journal of biomedical semantics · 2023Article
- Drug-disease association prediction with literature based multi-feature fusion.Frontiers in pharmacology · 2023Article
- A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology.Journal of biomedical semantics · 2022Article
- COVID-19 vaccine design using reverse and structural vaccinology, ontology-based literature mining and machine learning.Briefings in bioinformatics · 2022Article
- An Evaluation Model for the Influence Factors of Interest in Literature Courses Based on Data Analysis and Association Rules in a Small-Sample Environment.Journal of environmental and public health · 2022Article
- 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
- PathoPhenoDB, linking human pathogens to their phenotypes in support of infectious disease research.Scientific data · 2019Article
- Ontology based text mining of gene-phenotype associations: application to candidate gene prediction.Database : the journal of biological databases and curation · 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundInfectious diseases claim millions of lives especially in the developing countries each year. Identification of causative pathogens accurately and rapidly plays a key role in the success of treatment. To support infectious disease research and mechanisms of infection, there is a need for an open resource on pathogen-disease associations that can be utilized in computational studies. A large number of pathogen-disease associations is available from the literature in unstructured form and we need automated methods to extract the data.
resultsWe developed a text mining system designed for extracting pathogen-disease relations from literature. Our approach utilizes background knowledge from an ontology and statistical methods for extracting associations between pathogens and diseases. In total, we extracted a total of 3420 pathogen-disease associations from literature. We integrated our literature-derived associations into a database which links pathogens to their phenotypes for supporting infectious disease research.
conclusionsTo the best of our knowledge, we present the first study focusing on extracting pathogen-disease associations from publications. We believe the text mined data can be utilized as a valuable resource for infectious disease research. All the data is publicly available from https://github.com/bio-ontology-research-group/padimi and through a public SPARQL endpoint from http://patho.phenomebrowser.net/ .
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.