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.
Highlights
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
The presented algorithm delivers a considerable amount of information and may aid to accelerate the research and the automated analysis required for today's systems biology approaches
The short overall calculation time of the KID text mining algorithm and the resulting database prove evidence, that the presented algorithm can be a helpful tool for the annotation and collection of data for other databases like BRENDA
Summary
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. Several databases are available providing information about enzymes and their characteristics like e.g. BRENDA [2,3,4] with currently 92,291 entries for KM, 32,484 for kcat, 21,833 for Ki and 33,372 for specific activity [2], Kinetikon [5], KMedDB [6], KDBI [7], DOQCS [8], SABIO-RK [9] and IUPAC-kinetic [10], respectively These databases are far from complete, forcing scientists to a time consuming manual extraction of values from the literature if a systematic research approach is followed. Current algorithms include machine learning (e.g. Kinetikon [5]), statistic (e.g. FRENDA and AMENDA [3]), rule-based (KiPar [18] and BioRAT [19]) and mixed approaches (SUISEKI [20]).
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