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ArSInformatiCa

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Cerrado

Artificial intelligence branching processes and coalescent Searching the Infor...

Artificial intelligence branching processes and coalescent Searching the Information from a genetic Cornucopia The objective of the multidisciplinary ArSInformatiCa project is to reinforce the international dimension of a research career of European computer scientist by training him in complementary skills in a world-class research centre... ver más

30/09/2014

POLITECHNIKA SLASK...

206K€

Presupuesto del proyecto: 206K€

Líder del proyecto

POLITECHNIKA SLASKA No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2014-09-30 No tenemos la información de la convocatoria

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POLITECHNIKA SLASKA

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Líder del proyecto

POLITECHNIKA SLASKA No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 206K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto The objective of the multidisciplinary ArSInformatiCa project is to reinforce the international dimension of a research career of European computer scientist by training him in complementary skills in a world-class research centre, the George R. Brown School of Engineering at William M. Rice University in Houston, USA. The research will be carried out in two fields of information sciences: artificial intelligence (in particular machine learning) and distributed computer simulations applied to retrieval meaningful information from the whole-genome-scale variation data produced by high throughput genotyping technologies. In particular, the interest will be given to inherited predisposition to complex genetic diseases, including autoimmune diseases and cancers. An extremely large amount of data from the currently launched 1000 Genomes Project and Cancer Genome Project, requires a development of new advanced information technologies for understanding these data. This is an important challenge for information sciences, which motivates the goal of the ArSInformatiCa project. The progress in machine learning will not be limited to genetic applications. Rather, the methods developed, will be verified by application to human/cancer genetics with a potential to benefit wider and general context of data mining in very large and multidimensional data repositories. An example is the development of applicant’s rule-based method known as quasi-dominant rough set approach. While this method will be primarily tested in search for signatures of natural selection at molecular level in genes involved in human familial cancers, it is expected to become a general machine learning approach. Having a clear perspective of a long-term collaboration between Rice University and European research institutions, the ArSInformatiCa project will also contribute to the excellence of the European Research Area by a research inspired by the project results and performed after its completion.

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