Evidence map›Paper›PMID 20626859›Full record

ArticleBMC bioinformatics2010

KID--an algorithm for fast and efficient text mining used to automatically generate a database containing kinetic information of enzymes.

Stephanie Heinen, Bernhard Thielen, Dietmar Schomburg

Abstract read
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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.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Review
  2. 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 · 2016
    Article
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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Stephanie HeinenDpt. of Bioinformatics & Biochemistry, University of Braunschweig, Institute of Technology, Langer Kamp 19B, 38106 Braunschweig, Germany.
Bernhard Thielen
Dietmar Schomburg

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AlgorithmsDatabases, ProteinData MiningDictionaries as TopicEnzymesInternetKineticsEnzymes

Identifiers

PMID20626859
PMCPMC2912889

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LicenceCC BY
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Registered trials

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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.