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11th International conference on data integration in the life sciences
Dates: July 9-10, 2015
Venue: The University of Southern California, Los Angeles, CA, USA
Paper submission deadline for research, industry, application and experience papers: March 1, 2015
Notification of acceptance: April 15, 2015
Camera-ready copy due: May 15, 2015
Conference: July 9-10, 2015
Poster and demonstration papers (up to 4 pages): April 1 2015
The DILS conference aims to promote research and innovation in data integration, data sharing and data exchange in the domain of life sciences. The notion of life sciences (data) is expansive including the domains of biology and bioinformatics, biomedical data, medical data, and clinical and health information.
Researchers, informaticians, students and entrepreneurs from biology, medicine, computer science and engineering, and clinical informatics are invited to share their knowledge and experience.
DILS provides a forum for the discussion of challenges and technical solutions to address data integration and management in the life sciences. The advent of “big data” in literally every domain within the area, coming from high-throughput analytical techniques, large clinical data repositories, biomedical literature and online resources, throw up exciting new opportunities accompanied with key challenges to be addressed.
Topics of interest include, but are not limited to:
Data integration systems for the life sciences
Common data models, elements, and standards
Ontology mappings and evolution
Large-scale data analysis for the life sciences
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
Schema-matching in life sciences datasets
Privacy-preserving data integration and management
Data owner sensitive data sharing
Biomedical data integration issues in eScience
Laboratory information management systems in biology (including workflow systems)
Biomedical metadata management (including provenance)
Text analytics over unstructured data
Scientific results arising from innovative data integration solutions
Exposing biomedical data for integration (APIs, Linked Open Data, SPARQL endpoints)
Creation and use of clinical data repositories
Data integration in clinical and translational research
Integration of genotypic and phenotypic data
Ethical, legal and social issues with biomedical data integration
"Big Data" infrastructure applicability for life sciences
Virtual appliances for shared data analysis
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