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Semantic Interoperability and Data Mining in Biomedicine

Acronym: SEMANTICMINING
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Project Status History
unknown 12/17/2003
 
 Funding Programmes
CORDIS Network of Excellence5000000 

Abstract
The aim of this proposal for an Network of Excellence entitled Semantic Interoperability and Data Mining in Biomedicine is to establish Europe as the international scientific leader in medical and biomedical informatics. The long-term goal of the network will be the development of generic methods and tools supporting the critical tasks of the field; data mining, knowledge discovery, knowledge representation, abstraction and indexing of information, semantic-based information retrieval in a complex and high-dimensional information space, and knowledge-based adaptive systems for provision of decision support for dissemination of evidence based medicine. The proposal is a response to the strategic objectives addressed in the IST call 1, areas 'Semantic-based Knowledge Systems' and 'eHealth'. The general objective of the network is to bridge gaps in the European research infrastructure and to facilitate cross-fertilisation between scientific disciplines. Traditionally academic departments in the domain have their roots either in computer science, system engineering (including a variety of engineering disciplines) or in a medical or clinical context. The proposed network is composed of partners from these scientific areas, all bringing their experience and in-depths knowledge together into a common framework. An important aspect of this is the merging of medical or clinical informatics and bioinformatics including the new fields of genomics and proteomics. Another bridging activity addressed by the proposal is knowledge-transfer and co-operation between academia and organisations in the health and welfare sector, such as standardisation bodies and public and user-driven health care organisations. The NoE application is based on the partnership of 25 participants from 11 European countries with 99 identified researchers (25 female) and 31 associated PhD students (10 female).

 
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