ArticleBMC bioinformatics2010
KID--an algorithm for fast and efficient text mining used to automatically generate a database containing kinetic information of enzymes.
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 7 papers.
What it found
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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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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.
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Who cites it
7 citing papers in PubMed.
- Predictive Chemical Kinetic Modeling: Where We Succeed, Where We Struggle, and What Comes Next.ACS engineering Au · 2026Review
- Ion Channel ElectroPhysiology Ontology (ICEPO) - a case study of text mining assisted ontology development.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2016Article
- BRENDA in 2013: integrated reactions, kinetic data, enzyme function data, improved disease classification: new options and contents in BRENDA.Nucleic acids research · 2013Article
- Automated extraction of precise protein expression patterns in lymphoma by text mining abstracts of immunohistochemical studies.Journal of pathology informatics · 2013Article
- Automated extraction and semantic analysis of mutation impacts from the biomedical literature.BMC genomics · 2012Article
- Semantic annotation of biological concepts interplaying microbial cellular responses.BMC bioinformatics · 2011Article
- Discrete derivative: a data slicing algorithm for exploration of sharing biological networks between rheumatoid arthritis and coronary heart disease.BioData mining · 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
backgroundThe amount of available biological information is rapidly increasing and the focus of biological research has moved from single components to networks and even larger projects aiming at the analysis, modelling and simulation of biological networks as well as large scale comparison of cellular properties. It is therefore essential that biological knowledge is easily accessible. However, most information is contained in the written literature in an unstructured way, so that methods for the systematic extraction of knowledge directly from the primary literature have to be deployed. DESCRIPTION: Here we present a text mining algorithm for the extraction of kinetic information such as K(M), K(i), k(cat) etc. as well as associated information such as enzyme names, EC numbers, ligands, organisms, localisations, pH and temperatures. Using this rule- and dictionary-based approach, it was possible to extract 514,394 kinetic parameters of 13 categories (K(M), K(i), k(cat), k(cat)/K(M), V(max), IC(50), S(0.5), K(d), K(a), t(1/2), pI, n(H), specific activity, V(max)/K(M)) from about 17 million PubMed abstracts and combine them with other data in the abstract. A manual verification of approx. 1,000 randomly chosen results yielded a recall between 51% and 84% and a precision ranging from 55% to 96%, depending of the category searched.The results were stored in a database and are available as "KID the KInetic Database" via the internet.
conclusionsThe presented algorithm delivers a considerable amount of information and therefore may aid to accelerate the research and the automated analysis required for today's systems biology approaches. The database obtained by analysing PubMed abstracts may be a valuable help in the field of chemical and biological kinetics. It is completely based upon text mining and therefore complements manually curated databases. The database is available at http://kid.tu-bs.de. The source code of the algorithm is provided under the GNU General Public Licence and available on request from the author.
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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.