Integrating Clinical and Genomic Information Through the PrognoChip Mediator | Zendy

Anastasia Analyti | Zendy; Haridimos Kondylakis | Zendy; Dimitris Manakanatas | Zendy; Manos Kalaitzakis | Zendy; Dimitris Plexousakis | Zendy; George Potamias | Zendy

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Integrating Clinical and Genomic Information Through the PrognoChip Mediator

Author(s) -

Anastasia Analyti,

Haridimos Kondylakis,

Dimitris Manakanatas,

Manos Kalaitzakis,

Dimitris Plexousakis,

George Potamias

Publication year - 2006

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

ISBN - 3-540-68063-2

DOI - 10.1007/11946465_23

Subject(s) - computer science , informatics , information retrieval , health informatics , interface (matter) , identification (biology) , user interface , genomics , set (abstract data type) , health informatics tools , computational biology , data mining , gene , genome , medicine , biology , biochemistry , programming language , botany , nursing , bubble , maximum bubble pressure method , parallel computing , electrical engineering , operating system , engineering , public health

The ultimate goal of the biomedical informatics project PrognoChip is the identification of classification and prognosis molecular markers for breast cancer. This requires not only an understanding of the genetic basis of the disease, based on the patient's tumor gene expression profiles but also the correlation of this data with knowledge normally processed in the clinical setting. In this paper, we present the Mediator component of the PrognoChip Integrated Clinico-Genomics Environment (ICGE), through which the integration of the clinical and genomic information subsystems is achieved. The biomedical investigator can form clinico-genomic queries through the web-based graphical user interface of the Mediator. This is split into several query forms, allowing cancerous sample selection (along with their associated gene expression profiles and patient characteristics), based on criteria of interest. After a query is formed, the Mediator translates it into an equivalent set of local subqueries, which are executed directly against the constituent databases. Then, results are combined for presentation to the user and/or transmission to the Data Mining tools for analysis.

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