ArticleBMC bioinformatics2009
A realistic assessment of methods for extracting gene/protein interactions from free text.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Modeling genotype-protein interaction and correlation for Alzheimer's disease: a multi-omics imaging genetics study.Briefings in bioinformatics · 2024Article
- A Study of Biomedical Relation Extraction Using GPT Models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Text Mining and Machine Learning Protocol for Extracting Human-Related Protein Phosphorylation Information from PubMed.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Biomedical Literature Mining and Its Components.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Article
- Bridging semantics and syntax with graph algorithms-state-of-the-art of extracting biomedical relations.Briefings in bioinformatics · 2017Article
- Protein-protein interaction extraction with feature selection by evaluating contribution levels of groups consisting of related features.BMC bioinformatics · 2016Article
- PIPE: a protein-protein interaction passage extraction module for BioCreative challenge.Database : the journal of biological databases and curation · 2016Article
- Article
- Biocuration with insufficient resources and fixed timelines.Database : the journal of biological databases and curation · 2015Article
- Interaction relation ontology learning.Journal of computational biology : a journal of computational molecular cell biology · 2014Article
- Evaluating gold standard corpora against gene/protein tagging solutions and lexical resources.Journal of biomedical semantics · 2013Article
- Learning to recognize phenotype candidates in the auto-immune literature using SVM re-ranking.PloS one · 2013Article
- PPInterFinder--a mining tool for extracting causal relations on human proteins from literature.Database : the journal of biological databases and curation · 2013Article
- PIMiner: a web tool for extraction of protein interactions from biomedical literature.International journal of data mining and bioinformatics · 2013Article
- Using empirically constructed lexical resources for named entity recognition.Biomedical informatics insights · 2013Article
- Biomedical named entity extraction: some issues of corpus compatibilities.SpringerPlus · 2013Article
- Application and evaluation of automated methods to extract neuroanatomical connectivity statements from free text.Bioinformatics (Oxford, England) · 2012Article
- A text-mining system for extracting metabolic reactions from full-text articles.BMC bioinformatics · 2012Article
- Enhancing clinical concept extraction with distributional semantics.Journal of biomedical informatics · 2012Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe automated extraction of gene and/or protein interactions from the literature is one of the most important targets of biomedical text mining research. In this paper we present a realistic evaluation of gene/protein interaction mining relevant to potential non-specialist users. Hence we have specifically avoided methods that are complex to install or require reimplementation, and we coupled our chosen extraction methods with a state-of-the-art biomedical named entity tagger.
resultsOur results show: that performance across different evaluation corpora is extremely variable; that the use of tagged (as opposed to gold standard) gene and protein names has a significant impact on performance, with a drop in F-score of over 20 percentage points being commonplace; and that a simple keyword-based benchmark algorithm when coupled with a named entity tagger outperforms two of the tools most widely used to extract gene/protein interactions.
conclusionIn terms of availability, ease of use and performance, the potential non-specialist user community interested in automatically extracting gene and/or protein interactions from free text is poorly served by current tools and systems. The public release of extraction tools that are easy to install and use, and that achieve state-of-art levels of performance should be treated as a high priority by the biomedical text mining community.
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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.