ArticleFood microbiology2019
Text mining tools for extracting information about microbial biodiversity in food.
Article in Food microbiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- MINERVA-microbiome network research and visualization atlas: a scalable knowledge graph for mapping microbiome-disease associations.Briefings in bioinformatics · 2025Article
- Unsupervised literature mining approaches for extracting relationships pertaining to habitats and reproductive conditions of plant species.Frontiers in artificial intelligence · 2024Article
- Cluster-based text mining for extracting drug candidates for the prevention of COVID-19 from the biomedical literature.Journal of Taibah University Medical Sciences · 2023Article
- Classifying literature mentions of biological pathogens as experimentally studied using natural language processing.Journal of biomedical semantics · 2023Article
- Omnicrobe, an open-access database of microbial habitats and phenotypes using a comprehensive text mining and data fusion approach.PloS one · 2023Article
- Small molecule modulation of microbiota: a systems pharmacology perspective.BMC bioinformatics · 2022Article
- A machine learning framework for discovery and enrichment of metagenomics metadata from open access publications.GigaScience · 2022Article
- Inferring microbiota functions from taxonomic genes: a review.GigaScience · 2022Review
- Text mining for identification of biological entities related to antibiotic resistant organisms.PeerJ · 2022Article
- A Bibliometric Analysis on Dengue Outbreaks in Tropical and Sub-Tropical Climates Worldwide Since 1950.International journal of environmental research and public health · 2021Article
- Bipartite Network of Interest (BNOI): Extending Co-Word Network with Interest of Researchers Using Sensor Data and Corresponding Applications as an Example.Sensors (Basel, Switzerland) · 2021Article
- COPIOUS: A gold standard corpus of named entities towards extracting species occurrence from biodiversity literature.Biodiversity data journal · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Information on food microbial diversity is scattered across millions of scientific papers. Researchers need tools to assist their bibliographic search in such large collections. Text mining and knowledge engineering methods are useful to automatically and efficiently find relevant information in Life Science. This work describes how the Alvis text mining platform has been applied to a large collection of PubMed abstracts of scientific papers in the food microbiology domain. The information targeted by our work is microorganisms, their habitats and phenotypes. Two knowledge resources, the NCBI taxonomy and the OntoBiotope ontology were used to detect this information in texts. The result of the text mining process was indexed and is presented through the AlvisIR Food on-line semantic search engine. In this paper, we also show through two illustrative examples the great potential of this new tool to assist in studies on ecological diversity and the origin of microbial presence in food.
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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.