Workshop Chair

Isabelle Bichindaritz, University of Washington, USA
Mykola Pechenizkiy, Eindhoven University of Technology, The Netherlands

Workshop Committee

• Riccardo Bellazzi, University of Pavia, Italy
• Kung-Ma Chao, National Taiwan University, Taiwan
• Michel Dojat, UM INSERM-UJF U594 , France
• Peter Funk, Malardalen University, Sweden
• Sophia Katrenko University of Amsterdam, the Netherlands
• Xiaoqiu Huang, Iowa state University, USA
• Jingchu Luo, Peking University, China
• Stefania Montani, University of Piemonte Orientale, Italy
• Oleg Okun, Precise Biometrics, Sweden
• Petra Perner, Institute of Computer Vision and Applied Computer Sciences, Germany
• Frank-Michael Schleif, University Leipzig, Germany
• Rainer Schmidt, Institut fur Medizinische Informatik und Biometrie, Germany
• Malika Smail-Tabbone, LORIA, France
• Paolo Soda, Università Campus Bio-Medico di Roma, Italy
• Herna L. Viktor, University of Ottawa, Canada

Scope of the Workshop

Data mining in biology and medicine is a core component of biomedical informatics, and one of the first intensive applications of computer science to this field, whether at the clinic, the laboratory, or the research center. Following a long tradition of data exploration stemming from biostatistical data analysis, todays's biomedical data mining appears more multifaceted with advances in knowledge discovery in databases as well as machine learning approaches.

The goals of this workshop are to:

• provide a forum for identifying important contributions and opportunities for research on data mining as it applies to biological and/or medical data,
• promote the systematic study of how to apply data mining to biology and medicine, and
• show case applications of data mining in biology and medicine.

Some of the technical issues addressed, and potential outcomes of the workshop, are to identify preferred types of mining methods, tools, and processes, preferred domains of application, how to connect a data mining model with a problem to solve, challenges specific to applying data mining to biology and medicine, and guidelines to better develop data mining projects in this domain. We welcome all those interested in the problems and promise of data mining in biology or medicine as well as in bioinformatics, Human Genome Project, environmental sciences and agriculture.

Topics of interest include (but are not limited to):

With regard to different types of data:

• Discovery of high-level structures, including e.g. association networks
• Text mining from biomedical literatur
• Medical images mining
• Biomedical signals mining
• Temporal and sequential data mining
• Mining heterogeneous data
• Mining data from molecular biology, genomics, proteomics, pylogenetic classification

With regard to different methodologies and case studies:

• Data mining project development methodology for biomedicine
• Integration of data mining in the clinic
• Ontology-driver data mining in life sciences
• Methodology for mining complex data, e.g. a combination of laboratory test results, images, signals, genomic and proteomic samples
• Data mining for personal disease management
• Utility considerations in DMLS, including e.g. cost-sensitive learning
• We particularly welcome case studies and applications and discussions of the lessons learned from such case studies

Workshop Format

In this workshop we intend to bring scientists together and actively identify common research threads, define open problems, and develop collaborative contacts. We aimed at providing an informal atmosphere where participants are encouraged to ask clarifying questions throughout the talks and to participate in longer discussions after each presentation. Since we anticipate varied backgrounds of the participants, we will encourage speakers to present their work from a big-picture perspective and to clearly identify key issues in their research before they dive into technical details.

A wrap-up round table discussion will summarize the lessons learnt, issues identified, and future directions.

Submission Requirements