ArticleBMC bioinformatics2010
eGIFT: mining gene information from the literature.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Identification of conclusive association entities in biomedical articles.Journal of biomedical semantics · 2019Article
- WebGIVI: a web-based gene enrichment analysis and visualization tool.BMC bioinformatics · 2017Article
- Analysis of Protein Phosphorylation and Its Functional Impact on Protein-Protein Interactions via Text Mining of the Scientific Literature.Methods in molecular biology (Clifton, N.J.) · 2017Article
- Characterization of genes and pathways that respond to heat stress in Holstein calves through transcriptome analysis.Cell stress & chaperones · 2017Article
- miRiaD: A Text Mining Tool for Detecting Associations of microRNAs with Diseases.Journal of biomedical semantics · 2016Article
- Transcriptome response to heat stress in a chicken hepatocellular carcinoma cell line.Cell stress & chaperones · 2015Article
- Article
- Passage-Based Bibliographic Coupling: An Inter-Article Similarity Measure for Biomedical Articles.PloS one · 2015Article
- Identification of highly related references about gene-disease association.BMC bioinformatics · 2014Article
- The BioIntelligence Framework: a new computational platform for biomedical knowledge computing.Journal of the American Medical Informatics Association : JAMIA · 2013Article
- An overview of the BioCreative 2012 Workshop Track III: interactive text mining task.Database : the journal of biological databases and curation · 2013Article
- The eFIP system for text mining of protein interaction networks of phosphorylated proteins.Database : the journal of biological databases and curation · 2012Article
- Developing a biocuration workflow for AgBase, a non-model organism database.Database : the journal of biological databases and curation · 2012Article
- AgBase: supporting functional modeling in agricultural organisms.Nucleic acids research · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWith the biomedical literature continually expanding, searching PubMed for information about specific genes becomes increasingly difficult. Not only can thousands of results be returned, but gene name ambiguity leads to many irrelevant hits. As a result, it is difficult for life scientists and gene curators to rapidly get an overall picture about a specific gene from documents that mention its names and synonyms.
resultsIn this paper, we present eGIFT (http://biotm.cis.udel.edu/eGIFT), a web-based tool that associates informative terms, called iTerms, and sentences containing them, with genes. To associate iTerms with a gene, eGIFT ranks iTerms about the gene, based on a score which compares the frequency of occurrence of a term in the gene's literature to its frequency of occurrence in documents about genes in general. To retrieve a gene's documents (Medline abstracts), eGIFT considers all gene names, aliases, and synonyms. Since many of the gene names can be ambiguous, eGIFT applies a disambiguation step to remove matches that do not correspond to this gene. Another additional filtering process is applied to retain those abstracts that focus on the gene rather than mention it in passing. eGIFT's information for a gene is pre-computed and users of eGIFT can search for genes by using a name or an EntrezGene identifier. iTerms are grouped into different categories to facilitate a quick inspection. eGIFT also links an iTerm to sentences mentioning the term to allow users to see the relation between the iTerm and the gene. We evaluated the precision and recall of eGIFT's iTerms for 40 genes; between 88% and 94% of the iTerms were marked as salient by our evaluators, and 94% of the UniProtKB keywords for these genes were also identified by eGIFT as iTerms.
conclusionsOur evaluations suggest that iTerms capture highly-relevant aspects of genes. Furthermore, by showing sentences containing these terms, eGIFT can provide a quick description of a specific gene. eGIFT helps not only life scientists survey results of high-throughput experiments, but also annotators to find articles describing gene aspects and functions.
Indexed as
Identifiers
What OpenQuestion holds
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.