A large amount of data is currently being collected in bio-medicine for both experimental and observational purposes. This automatic data collection pushes towards the development of methods and tools able to handle and analyze data in a computer-supported fashion, and to support evidence to be exploited in all activities of the biomedical field. In the majority of the application areas, this task cannot be accomplished without using the available knowledge on the domain or on the data analysis process. This need becomes crucial in clinical applications, since medical decision making needs to be supported by arguments based on basic medical and pharmacological knowledge.

The topics of the workshop are computational methods for biomedical data analysis that aim to narrow the gap between data gathering and data comprehension and to support the use and exploitation of observational and retrospective data.

In terms of methodology, topics include, but are not limited to,

• data mining techniques, including machine learning, clustering, neural networks, etc.,
• other techniques for construction of predictive models,
• data visualization,
• interpretation of time-ordered data (derivation and revision of temporal trends and other forms of temporal data abstraction),
• knowledge management and its integration with intelligent data analysis techniques,
• utility of background knowledge in data analysis,
• integration of intelligent data analysis techniques within biomedical information systems.

Contributions that discuss particular applications of intelligent data analysis techniques are invited, especially those that cover:

• analysis of medical and health-care data;
• analysis of pharmacological data, drug design, drug testing; discovery of new drug compounds, pharmacodynamical modeling;
• exploration of bioinformatics data (protein structure prediction, functional genomics, analysis of gene expression data);
• outcomes analysis.

We further encourage the submission of papers that address question like:

• What are the application classes that motivate the usage of certain methods?
• What is the potential applicability (and generalizability) of proposed solutions?
• What is the level of integration with other methods and tools to achieve real working systems?
• What kind of knowledge is needed, used and/or extracted by the IDA and DM methods?
• What is the role of prior knowledge in data analysis?
• How should the available knowledge be represented?