ArticleJournal of biomedical semantics2012
Literature mining of protein-residue associations with graph rules learned through distant supervision.
Article in Journal of biomedical semantics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 51 citations in OpenAlex.
- Associating protein residues in the literature with structural data.Acta crystallographica. Section D, Structural biology · 2026Article
- Automatic extraction of ranked SNP-phenotype associations from text using a BERT-LSTM-based method.BMC bioinformatics · 2023Article
- Named Entity Recognition and Relation Detection for Biomedical Information Extraction.Frontiers in cell and developmental biology · 2020Review
- CoCoScore: context-aware co-occurrence scoring for text mining applications using distant supervision.Bioinformatics (Oxford, England) · 2020Article
- Automatic annotation of protein residues in published papers.Acta crystallographica. Section F, Structural biology communications · 2019Article
- Automated assessment of biological database assertions using the scientific literature.BMC bioinformatics · 2019Article
- BioCreative VI Precision Medicine Track system performance is constrained by entity recognition and variations in corpus characteristics.Database : the journal of biological databases and curation · 2018Article
- nala: text mining natural language mutation mentions.Bioinformatics (Oxford, England) · 2017Article
- SNPPhenA: a corpus for extracting ranked associations of single-nucleotide polymorphisms and phenotypes from literature.Journal of biomedical semantics · 2017Article
- Extracting microRNA-gene relations from biomedical literature using distant supervision.PloS one · 2017Article
- Bridging semantics and syntax with graph algorithms-state-of-the-art of extracting biomedical relations.Briefings in bioinformatics · 2017Article
- Predicting the functions of a protein from its ability to associate with other molecules.BMC bioinformatics · 2016Article
- Improving chemical disease relation extraction with rich features and weakly labeled data.Journal of cheminformatics · 2016Article
- Community challenges in biomedical text mining over 10 years: success, failure and the future.Briefings in bioinformatics · 2016Review
- Text mining facilitates database curation - extraction of mutation-disease associations from Bio-medical literature.BMC bioinformatics · 2015Article
- Discovering relations between indirectly connected biomedical concepts.Journal of biomedical semantics · 2015Article
- Mutation extraction tools can be combined for robust recognition of genetic variants in the literature.F1000Research · 2014Article
- Literature mining of genetic variants for curation: quantifying the importance of supplementary material.Database : the journal of biological databases and curation · 2014Article
- Associating disease-related genetic variants in intergenic regions to the genes they impact.PeerJ · 2014Article
- Approximate subgraph matching-based literature mining for biomedical events and relations.PloS one · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundWe propose a method for automatic extraction of protein-specific residue mentions from the biomedical literature. The method searches text for mentions of amino acids at specific sequence positions and attempts to correctly associate each mention with a protein also named in the text. The methods presented in this work will enable improved protein functional site extraction from articles, ultimately supporting protein function prediction. Our method made use of linguistic patterns for identifying the amino acid residue mentions in text. Further, we applied an automated graph-based method to learn syntactic patterns corresponding to protein-residue pairs mentioned in the text. We finally present an approach to automated construction of relevant training and test data using the distant supervision model.
resultsThe performance of the method was assessed by extracting protein-residue relations from a new automatically generated test set of sentences containing high confidence examples found using distant supervision. It achieved a F-measure of 0.84 on automatically created silver corpus and 0.79 on a manually annotated gold data set for this task, outperforming previous methods.
conclusionsThe primary contributions of this work are to (1) demonstrate the effectiveness of distant supervision for automatic creation of training data for protein-residue relation extraction, substantially reducing the effort and time involved in manual annotation of a data set and (2) show that the graph-based relation extraction approach we used generalizes well to the problem of protein-residue association extraction. This work paves the way towards effective extraction of protein functional residues from the literature.
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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.