ArticlePLoS computational biology2007
Automatic extraction of protein point mutations using a graph bigram association.
Article in PLoS computational biology, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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.
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Who cites it
24 citing papers in PubMed, 46 citations in OpenAlex.
- ResidueFinder: extracting individual residue mentions from protein literature.Journal of biomedical semantics · 2021Article
- Recent advances of automated methods for searching and extracting genomic variant information from biomedical literature.Briefings in bioinformatics · 2021Review
- MAGPEL: an autoMated pipeline for inferring vAriant-driven Gene PanEls from the full-length biomedical literature.Scientific reports · 2020Article
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- Text mining facilitates database curation - extraction of mutation-disease associations from Bio-medical literature.BMC bioinformatics · 2015Article
- Towards precision medicine: advances in computational approaches for the analysis of human variants.Journal of molecular biology · 2013Review
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- The curation of genetic variants: difficulties and possible solutions.Genomics, proteomics & bioinformatics · 2012Review
- Literature mining of protein-residue associations with graph rules learned through distant supervision.Journal of biomedical semantics · 2012Article
- Automated extraction and semantic analysis of mutation impacts from the biomedical literature.BMC genomics · 2012Article
- Interpretation of the consequences of mutations in protein kinases: combined use of bioinformatics and text mining.Frontiers in physiology · 2012Article
- Integrating GPCR-specific information with full text articles.BMC bioinformatics · 2011Article
- Toward an automatic method for extracting cancer- and other disease-related point mutations from the biomedical literature.Bioinformatics (Oxford, England) · 2011Article
- Challenges in the association of human single nucleotide polymorphism mentions with unique database identifiers.BMC bioinformatics · 2011Article
- Algorithms and semantic infrastructure for mutation impact extraction and grounding.BMC genomics · 2010Article
- Prospects for the automated extraction of mutation data from the scientific literature.Human genomics · 2010Article
- GATMD: γ-aminobutyric acid transporter mutagenesis database.Database : the journal of biological databases and curation · 2010Article
- EnzyMiner: automatic identification of protein level mutations and their impact on target enzymes from PubMed abstracts.BMC bioinformatics · 2009Article
- Extraction of human kinase mutations from literature, databases and genotyping studies.BMC bioinformatics · 2009Article
- Annotation of protein residues based on a literature analysis: cross-validation against UniProtKb.BMC bioinformatics · 2009Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
Protein point mutations are an essential component of the evolutionary and experimental analysis of protein structure and function. While many manually curated databases attempt to index point mutations, most experimentally generated point mutations and the biological impacts of the changes are described in the peer-reviewed published literature. We describe an application, Mutation GraB (Graph Bigram), that identifies, extracts, and verifies point mutations from biomedical literature. The principal problem of point mutation extraction is to link the point mutation with its associated protein and organism of origin. Our algorithm uses a graph-based bigram traversal to identify these relevant associations and exploits the Swiss-Prot protein database to verify this information. The graph bigram method is different from other models for point mutation extraction in that it incorporates frequency and positional data of all terms in an article to drive the point mutation-protein association. Our method was tested on 589 articles describing point mutations from the G protein-coupled receptor (GPCR), tyrosine kinase, and ion channel protein families. We evaluated our graph bigram metric against a word-proximity metric for term association on datasets of full-text literature in these three different protein families. Our testing shows that the graph bigram metric achieves a higher F-measure for the GPCRs (0.79 versus 0.76), protein tyrosine kinases (0.72 versus 0.69), and ion channel transporters (0.76 versus 0.74). Importantly, in situations where more than one protein can be assigned to a point mutation and disambiguation is required, the graph bigram metric achieves a precision of 0.84 compared with the word distance metric precision of 0.73. We believe the graph bigram search metric to be a significant improvement over previous search metrics for point mutation extraction and to be applicable to text-mining application requiring the association of words.
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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.