Data integration in the life sciences

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The rise of high-throughput experimental methods in life sciences is generating vast and complex data resources essential for studying organisms and diseases. This experimental data forms the basis for developing biological system models, which often require integrating diverse data sets. Once models are created, they generate new data and pose integration challenges related to interpretation, validation, and comparison. The Data Integration in the Life Sciences (DILS) Conference series unites data and knowledge management researchers from computer science with bioinformaticians and computational biologists to explore how emerging data integration techniques can meet the needs of the life sciences. DILS 2010, the seventh event in this series, took place in Gothenburg, Sweden, from August 25–27, 2010. The proceedings feature 14 peer-reviewed papers and 2 invited papers, covering topics such as ontology engineering—including evolution, matching, and debugging, which are crucial for semantic integration—Web services for data integration, and data and text mining techniques for identifying biomedical entities and their relationships. Additionally, the sessions introduced data integration solutions for applications in cancer, systems biology, and microarray experimental data, as well as methods for integrating ranked data in the life sciences.