The Grid represents a great opportunity in the scientific community to solve data/compute intensive problems and to share data. Using the Grid, different application domains could achieve great advantages; however the exploitation of the Grid is nota trivial pursuit for many users. The key issue is to enable a simplified use of Grid resources and tools that scientists usually employ. In this paper we investigate these topics in bioinformatics community to allow the efficient elaboration on the Grid of images obtained through the Tissue MicroArray technique. We present a Grid Framework for Tissue Microarray Analysis, GF4TMA, that allows the selection of TMA images and their efficient analysis by the exploitation of the Grid architecture. In particular, we exploited PIMA(GE)(2) Lib, the Parallel IMAGE processing GEnoa Library. A critical point was to enable parallel computations on the Grid without compromising the efficiency and the user-friendliness of the native library. We tackled this issue by encapsulating the library in a Grid service.
Enabling parallel TMA image analysis in a grid environment
Galizia Antonella;Viti Federica;Orro Alessandro;Merelli Ivan;Milanesi Luciano
2008
Abstract
The Grid represents a great opportunity in the scientific community to solve data/compute intensive problems and to share data. Using the Grid, different application domains could achieve great advantages; however the exploitation of the Grid is nota trivial pursuit for many users. The key issue is to enable a simplified use of Grid resources and tools that scientists usually employ. In this paper we investigate these topics in bioinformatics community to allow the efficient elaboration on the Grid of images obtained through the Tissue MicroArray technique. We present a Grid Framework for Tissue Microarray Analysis, GF4TMA, that allows the selection of TMA images and their efficient analysis by the exploitation of the Grid architecture. In particular, we exploited PIMA(GE)(2) Lib, the Parallel IMAGE processing GEnoa Library. A critical point was to enable parallel computations on the Grid without compromising the efficiency and the user-friendliness of the native library. We tackled this issue by encapsulating the library in a Grid service.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
