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

ABSTRACT: BACKGROUND: The 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. CONCLUSIONS: The 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.

SUBMITTER: Heinen S

PROVIDER: S-EPMC2912889 | biostudies-other | 2010

REPOSITORIES: biostudies-other

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

Heinen Stephanie S Thielen Bernhard B Schomburg Dietmar D

BMC bioinformatics 20100713

<h4>Background</h4>The 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 meth ...[more]

PMID: 20626859

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Project description:BACKGROUND:Blood chemicals are routinely measured in clinical or preclinical research studies to diagnose diseases, assess risks in epidemiological research, or use metabolomic phenotyping in response to treatments. A vast volume of blood-related literature is available via the PubMed database for data mining. OBJECTIVES:We aimed to generate a comprehensive blood exposome database of endogenous and exogenous chemicals associated with the mammalian circulating system through text mining and database fusion. METHODS:Using NCBI resources, we retrieved PubMed abstracts, PubChem chemical synonyms, and PMC supplementary tables. We then employed text mining and PubChem crowdsourcing to associate phrases relating to blood with PubChem chemicals. False positives were removed by a phrase pattern and a compound exclusion list. RESULTS:A query to identify blood-related publications in the PubMed database yielded 1.1 million papers. Matching a total of 15 million synonyms from 6.5 million relevant PubChem chemicals against all blood-related publications yielded 37,514 chemicals and 851,999 publications records. Mapping PubChem compound identifiers to the PubMed database yielded 49,940 unique chemicals linked to 676,643 papers. Analysis of open-access metabolomics papers related to blood phrases in the PMC database yielded 4,039 unique compounds and 204 papers. Consolidating these three approaches summed up to a total of 41,474 achiral structures that were linked to 65,957 PubChem CIDs and to over 878,966 PubMed articles. We mapped these compounds to 50 databases such as those covering metabolites and pathways, governmental and toxicological databases, pharmacology resources, and bioassay repositories. In comparison, HMDB, the Human Metabolome Database, links 1,075 compounds to blood-related primary publications. CONCLUSION:This new Blood Exposome Database can be used for prioritizing chemicals for systematic reviews, developing target assays in exposome research, identifying compounds in untargeted mass spectrometry, and biological interpretation in metabolomics data. The database is available at http://bloodexposome.org. https://doi.org/10.1289/EHP4713.

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Dataset Information

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

Publications

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

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