For several years now, there has been an exponential growth of the amount of life science data (e.g., sequenced complete genomes, 3D structures, DNA chips, Mass spectroscopy data) generated by high throughput experiments. Large amounts of data are distributed across many sources over the web, with high degree of semantic heterogeneity and different levels of quality. These data must be combined with other data and processed by bioinformatics tools deployed on powerful and efficient platforms for patterns, similarities, and unusual occurrences to be observed. Carrying out analyses of complex, voluminous, and heterogeneous data and guiding the analysis of data is thus of paramount importance and necessitates data integration techniques to be involved.

DILS 2008 is the fifth in a workshop series that aims at fostering discussion, exchange, and innovation in research and development in the areas of data integration and data management for the life sciences. DILS 2004 in Leipzig, DILS 2005 in San Diego, DILS 2006 in Hinxton, and DILS 2007 in Philadelphia, each attracted around 100 researchers from all over the world. We invite researchers, and professionals from biology, medicine, computer science, and engineering to participate and share their knowledge in this forum.

Papers must address challenges for data integration and data management in the life sciences.

Topics of interest include, but are not limited to:

* Architectures and data management techniques for the life sciences
* Query processing and optimization for biological data
* Biological Data sharing and update propagation
* Query formulation assistance for scientists
* Modeling of life sciences data
* Biological Metadata management
* Annotation in scientific data integration
* Provenance modeling and management for the life sciences
* Scientific Workflows and analysis pipelines
* Laboratory information management systems in biology
* Biological data quality and data cleaning
* Life sciences ontologies
* Semantic web for the life sciences
* Mining integrated life sciences data
* Machine learning in data integration in the life sciences
* Grid Computing and Grid technologies for the life sciences
* System prototypes for biology
* Commercial solutions in the life sciences


PC chairs

* Amos Bairoch , Swiss Institute of Bioinformatics, Swiss-Prot group, University of Geneva, Switzerland
* Sarah Cohen Boulakia , Laboratoire de Recherche en Informatique, University of Paris-Sud XI, France
* Christine Froidevaux , Laboratoire de Recherche en Informatique, University of Paris-Sud XI, France