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

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.DescriptionHere we present a text mining algorithm for the extraction of kinetic information such as KM, Ki, kcat 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 (KM, Ki, kcat, kcat/KM, Vmax, IC50, S0.5, Kd, Ka, t1/2, pI, nH, specific activity, Vmax/KM) 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.

BRENDA in 2013: integrated reactions, kinetic data, enzyme function data, improved disease classification: new options and contents in BRENDA

“…Nevertheless they cannot cover the entire enzyme literature. Recently this gap could be filled with data from a text mining method adapted from a previously developed method (29). The procedure is based on text interpretation and supported by dictionaries with ∼2000 collected kinetic terms and units (including different spellings), ∼2000 terms for the interpretation of the sentence structure (e.g.…”

Automated extraction of precise protein expression patterns in lymphoma by text mining abstracts of immunohistochemical studies

“…A similar approach taking advantage of a limited dictionary of forms of expression of essential information about enzyme kinetics was employed by Heinen et al . [14] Caporaso et al .,[15] relied upon regular expressions to identify point mutations in genes.…”

A Text Mining Approach for the Extraction of Kinetic Information from Literature

“…However, it only analyzes abstracts and titles [15]. Using a similar approach, the KID algorithm extracts kinetic information for enzymes from abstracts and automatically generates a database [10]. Other approaches based on machine learning [14] have also been focused on abstracts, therefore excluding the information contained in full text documents.…”