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

Background 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 challe...

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Vydané v:BMC bioinformatics Ročník 14; číslo 1; s. 191
Hlavní autori: Holzinger, Andreas, Zupan, Mario
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London BioMed Central 13.06.2013
BioMed Central Ltd
Springer Nature B.V
Predmet:
Algorithms
Bioinformatics
Biomedical and Life Sciences
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Data mining
Data Mining - methods
Databases, Factual
Internet software
Life Sciences
Medical research
Medicine, Experimental
Microarrays
Networks analysis
Software
Technology application
Web applications
Austria
Knowledge discovery
Data analytics
Methods
ISSN:1471-2105, 1471-2105
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Shrnutí:Background 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. Results 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. Conclusions 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.
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ISSN:1471-2105
1471-2105
DOI:10.1186/1471-2105-14-191