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e-LICO

Financiado

Cerrado

e Laboratory for Interdisciplinary Collaborative Research in Data Mining and Dat...

e Laboratory for Interdisciplinary Collaborative Research in Data Mining and Data Intensive Sciences The goal of the e-LICO project is to build a virtual laboratory for interdisciplinary collaborative research in data mining and data-intensive sciences. The proposed e-lab will comprise three layers: the e-science and data mining... ver más

31/01/2012

UNIGE

4M€

Presupuesto del proyecto: 4M€

Líder del proyecto

UNIVERSITE DE GENEVE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 10 Participantes

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2012-01-31 No tenemos la información de la convocatoria

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

10 Participantes

UNIGE

0.00€ | Lider

RapidMiner

N.D. | Participante

NATIONAL HELLENIC RESEARCH F...

N.D. | Participante

RBI

N.D. | Participante

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Líder del proyecto

UNIVERSITE DE GENEVE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 4M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto The goal of the e-LICO project is to build a virtual laboratory for interdisciplinary collaborative research in data mining and data-intensive sciences. The proposed e-lab will comprise three layers: the e-science and data mining layers will form a generic research environment that can be adapted to different scientific domains by customizing the application layer. The e-science layer, built on an open-source e-science infrastructure developed by one of the partners, will support content creation through collaboration at multiple scales and degrees of commitment---ranging from small, contract-bound teams to voluntary, constraint-free participation in dynamic virtual communities. The data mining layer will be the distinctive core of e-LICO; it will provide comprehensive multimedia (structured records, text, images, signals) data mining tools. Standard tools will be augmented with preprocessing or learning algorithms developed specifically to meet challenges of data-intensive, knowledge rich sciences, such as ultra-high dimensionality or undersampled data. Methodologically sound use of these tools will be ensured by a knowledge-driven data mining assistant, which will rely on a data mining ontology and knowledge base to plan the mining process and propose ranked workflows for a given application problem. Extensive e-lab monitoring facilities will automate the accumulation of experimental meta-data to support replication and comparison of data mining experiments. These meta-data will be used by a meta-miner, which will combine probabilistic reasoning with kernel-based learning from complex structures to incrementally improve the assistant's workflow recommendations. e-LICO will be showcased in a systems biology task: biomarker discovery and molecular pathway modelling for diseases affecting the kidney and urinary pathways.

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