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

BMC Bioinformatics

Software

Mario Zupan¹  Andreas Holzinger² 

[1] Research Unit Human-Computer Interaction (HCI4MED), Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036, Graz, Austria;Research Unit Human-Computer Interaction (HCI4MED), Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036, Graz, Austria;Institute for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010, Graz, Austria;Institute for Information Systems and Computer Media, Graz University of Technology, Inffeldgasse 16c, 8010, Graz, Austria;

关键词: Knowledge discovery;    Methods;    Data analytics;   

DOI  :  10.1186/1471-2105-14-191

 received in 2013-02-28, accepted in 2013-05-31,  发布年份 2013

来源: Springer

【 摘 要 】

BackgroundProfessionals 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.ResultsA 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.ConclusionsThe 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.

【 授权许可】

CC BY   
© Holzinger and Zupan; licensee BioMed Central Ltd. 2013