Advances in data mining for biomedical research
This keynote lecture was given during the ECCB'14 conference, Tuesday, September 9, 2014, Palais de la Musique et des Congrès, Strasbourg, France. Professor Nada Lavrac was introduced by Dr. Malika Smaïl-Tabbone, University of Lorraine, France.
The talk will first outline standard approaches to data mining, with the emphasis on subgroup discovery which proves to be an effective tool for data analysis in biomedical applications. The core of the talk will be devoted to inductive logic programming and relational
data mining which also have a great potential for biomedical research, with a focus on recently developed approaches to semantic data mining, which enable the use of domain ontologies as background knowledge in data analysis. The use of described techniques and tools will be illustrated on selected biomedical applications. Moreover, the talk will present several data mining workflows, implemented in the recently developed platforms Orange4WS and ClowdFlows, which implement advanced methodologies which can be reused in biomedical applications.
Jožef Stefan Institute, Ljubljana and University of Nova Gorica, Nova Gorcia, Slovenia
Professor, Head of Department of Knowledge Technologies
Bio: Nada Lavrač is Head of Department of Knowledge Technologies at Jozef Stefan Institute, Ljubljana, Slovenia. She is also Professor at the Jozef Stefan International Postgraduate School in Ljubljana and at the University of Nova Gorica. Her main research interests are in Knowledge Technologies, with particular interests in machine learning, data mining, text mining, knowledge management and computational creativity. She is author of several books, including the recently published Foundations of Rule Learning, Springer 2012. Her special interest is in supervised descriptive rule induction, where the research goal is to automatically induce rules from class labeled data, stored either in simple tabular format or in complex relational databases. Areas of her applied research include data mining applications in medicine, health care and bioinformatics.
