ArticlePloS one2011
Named entity recognition for bacterial Type IV secretion systems.
Article in PloS one, 2011. 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
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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
10 citing papers in PubMed.
- Conjugative type IV secretion systems enable bacterial antagonism that operates independently of plasmid transfer.Communications biology · 2024Article
- A survey on clinical natural language processing in the United Kingdom from 2007 to 2022.NPJ digital medicine · 2022Review
- Text mining tools for extracting information about microbial biodiversity in food.Food microbiology · 2019Article
- Secretome of obligate intracellular Rickettsia.FEMS microbiology reviews · 2015Review
- Informatics-Driven Infectious Disease Research.Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference) · 2013Article
- Overview of the ID, EPI and REL tasks of BioNLP Shared Task 2011.BMC bioinformatics · 2012Article
- Prioritizing PubMed articles for the Comparative Toxicogenomic Database utilizing semantic information.Database : the journal of biological databases and curation · 2012Article
- Combined SVM-CRFs for biological named entity recognition with maximal bidirectional squeezing.PloS one · 2012Article
- PATRIC: the comprehensive bacterial bioinformatics resource with a focus on human pathogenic species.Infection and immunity · 2011Review
- The expanding bacterial type IV secretion lexicon.Research in microbiologyReview
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Research on specialized biological systems is often hampered by a lack of consistent terminology, especially across species. In bacterial Type IV secretion systems genes within one set of orthologs may have over a dozen different names. Classifying research publications based on biological processes, cellular components, molecular functions, and microorganism species should improve the precision and recall of literature searches allowing researchers to keep up with the exponentially growing literature, through resources such as the Pathosystems Resource Integration Center (PATRIC, patricbrc.org). We developed named entity recognition (NER) tools for four entities related to Type IV secretion systems: 1) bacteria names, 2) biological processes, 3) molecular functions, and 4) cellular components. These four entities are important to pathogenesis and virulence research but have received less attention than other entities, e.g., genes and proteins. Based on an annotated corpus, large domain terminological resources, and machine learning techniques, we developed recognizers for these entities. High accuracy rates (>80%) are achieved for bacteria, biological processes, and molecular function. Contrastive experiments highlighted the effectiveness of alternate recognition strategies; results of term extraction on contrasting document sets demonstrated the utility of these classes for identifying T4SS-related documents.
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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.