ArticleBMC bioinformatics2018
LocText: relation extraction of protein localizations to assist database curation.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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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
8 citing papers in PubMed.
- BioKGrapher: Initial evaluation of automated knowledge graph construction from biomedical literature.Computational and structural biotechnology journal · 2024Article
- Creation of a structured solar cell material dataset and performance prediction using large language models.Patterns (New York, N.Y.) · 2024Article
- PCfun: a hybrid computational framework for systematic characterization of protein complex function.Briefings in bioinformatics · 2022Article
- Spectrum of Protein Location in Proteomes Captures Evolutionary Relationship Between Species.Journal of molecular evolution · 2021Article
- CoCoScore: context-aware co-occurrence scoring for text mining applications using distant supervision.Bioinformatics (Oxford, England) · 2020Article
- Constructing knowledge graphs and their biomedical applications.Computational and structural biotechnology journal · 2020Review
- UPCLASS: a deep learning-based classifier for UniProtKB entry publications.Database : the journal of biological databases and curation · 2020Article
- Using distant supervision to augment manually annotated data for relation extraction.PloS one · 2019Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
backgroundThe subcellular localization of a protein is an important aspect of its function. However, the experimental annotation of locations is not even complete for well-studied model organisms. Text mining might aid database curators to add experimental annotations from the scientific literature. Existing extraction methods have difficulties to distinguish relationships between proteins and cellular locations co-mentioned in the same sentence.
resultsLocText was created as a new method to extract protein locations from abstracts and full texts. LocText learned patterns from syntax parse trees and was trained and evaluated on a newly improved LocTextCorpus. Combined with an automatic named-entity recognizer, LocText achieved high precision (P = 86%±4). After completing development, we mined the latest research publications for three organisms: human (Homo sapiens), budding yeast (Saccharomyces cerevisiae), and thale cress (Arabidopsis thaliana). Examining 60 novel, text-mined annotations, we found that 65% (human), 85% (yeast), and 80% (cress) were correct. Of all validated annotations, 40% were completely novel, i.e. did neither appear in the annotations nor the text descriptions of Swiss-Prot.
conclusionsLocText provides a cost-effective, semi-automated workflow to assist database curators in identifying novel protein localization annotations. The annotations suggested through text-mining would be verified by experts to guarantee high-quality standards of manually-curated databases such as Swiss-Prot.
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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.