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KNODWAT: A scientific framework application for testing knowledge discovery methods for the biomedical domain

Andreas Holzinger123* and Mario Zupan1

Author Affiliations

1 Research Unit Human-Computer Interaction (HCI4MED), Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, Graz 8036, Austria

2 Institute for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, Graz 8010, Austria

3 Institute for Information Systems and Computer Media, Graz University of Technology, Inffeldgasse 16c, Graz 8010, Austria

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BMC Bioinformatics 2013, 14:191  doi:10.1186/1471-2105-14-191

Published: 13 June 2013



Professionals in the biomedical domain are confronted with an increasing mass of data. Developing methods to assist professional end users in the field of Knowledge Discovery to identify, extract, visualize and understand useful information from these huge amounts of data is a huge challenge. However, there are so many diverse methods and methodologies available, that for biomedical researchers who are inexperienced in the use of even relatively popular knowledge discovery methods, it can be very difficult to select the most appropriate method for their particular research problem.


A web application, called KNODWAT (KNOwledge Discovery With Advanced Techniques) has been developed, using Java on Spring framework 3.1. and following a user-centered approach. The software runs on Java 1.6 and above and requires a web server such as Apache Tomcat and a database server such as the MySQL Server. For frontend functionality and styling, Twitter Bootstrap was used as well as jQuery for interactive user interface operations.


The framework presented is user-centric, highly extensible and flexible. Since it enables methods for testing using existing data to assess suitability and performance, it is especially suitable for inexperienced biomedical researchers, new to the field of knowledge discovery and data mining. For testing purposes two algorithms, CART and C4.5 were implemented using the WEKA data mining framework.

Knowledge discovery; Methods; Data analytics