ArticleBMC bioinformatics2009
EnzyMiner: automatic identification of protein level mutations and their impact on target enzymes from PubMed abstracts.
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 17 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
17 citing papers in PubMed.
- Cutting-Edge AI Technologies Meet Precision Medicine to Improve Cancer Care.Biomolecules · 2022Review
- Contextualizing Genes by Using Text-Mined Co-Occurrence Features for Cancer Gene Panel Discovery.Frontiers in genetics · 2021Article
- MAGPEL: an autoMated pipeline for inferring vAriant-driven Gene PanEls from the full-length biomedical literature.Scientific reports · 2020Article
- Text Mining Genotype-Phenotype Relationships from Biomedical Literature for Database Curation and Precision Medicine.PLoS computational biology · 2016Article
- Text mining for precision medicine: automating disease-mutation relationship extraction from biomedical literature.Journal of the American Medical Informatics Association : JAMIA · 2016Article
- Article
- Benchmarking infrastructure for mutation text mining.Journal of biomedical semantics · 2014Article
- Towards precision medicine: advances in computational approaches for the analysis of human variants.Journal of molecular biology · 2013Review
- tmVar: a text mining approach for extracting sequence variants in biomedical literature.Bioinformatics (Oxford, England) · 2013Article
- Mining the pharmacogenomics literature--a survey of the state of the art.Briefings in bioinformatics · 2012Article
- Automated extraction and semantic analysis of mutation impacts from the biomedical literature.BMC genomics · 2012Article
- Development of a classification scheme for disease-related enzyme information.BMC bioinformatics · 2011Article
- Toward an automatic method for extracting cancer- and other disease-related point mutations from the biomedical literature.Bioinformatics (Oxford, England) · 2011Article
- A formalized description of the standard human variant nomenclature in Extended Backus-Naur Form.BMC bioinformatics · 2011Article
- Algorithms and semantic infrastructure for mutation impact extraction and grounding.BMC genomics · 2010Article
- Between proteins and phenotypes: annotation and interpretation of mutations.BMC bioinformatics · 2009Article
- Automated Metabolic Phenotyping of Cytochrome Polymorphisms Using PubMed Abstract Mining.AMIA ... Annual Symposium proceedings. AMIA SymposiumArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA better understanding of the mechanisms of an enzyme's functionality and stability, as well as knowledge and impact of mutations is crucial for researchers working with enzymes. Though, several of the enzymes' databases are currently available, scientific literature still remains at large for up-to-date source of learning the effects of a mutation on an enzyme. However, going through vast amounts of scientific documents to extract the information on desired mutation has always been a time consuming process. In this paper, therefore, we describe an unique method, termed as EnzyMiner, which automatically identifies the PubMed abstracts that contain information on the impact of a protein level mutation on the stability and/or the activity of a given enzyme.
resultsWe present an automated system which identifies the abstracts that contain an amino-acid-level mutation and then classifies them according to the mutation's effect on the enzyme. In the case of mutation identification, MuGeX, an automated mutation-gene extraction system has an accuracy of 93.1% with a 91.5 F-measure. For impact analysis, document classification is performed to identify the abstracts that contain a change in enzyme's stability or activity resulting from the mutation. The system was trained on lipases and tested on amylases with an accuracy of 85%.
conclusionEnzyMiner identifies the abstracts that contain a protein mutation for a given enzyme and checks whether the abstract is related to a disease with the help of information extraction and machine learning techniques. For disease related abstracts, the mutation list and direct links to the abstracts are retrieved from the system and displayed on the Web. For those abstracts that are related to non-diseases, in addition to having the mutation list, the abstracts are also categorized into two groups. These two groups determine whether the mutation has an effect on the enzyme's stability or functionality followed by displaying these on the web.
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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.