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A grid environment for high-throughput proteomics

Academic Article
Publication Date:
2007
abstract:
We connect in a grid-enabled pipeline an ontology-based environment for proteomics spectra management with a machine learning platform for unbiased predictive analysis. We exploit two existing software platforms (MS-Analyzer and BioDCV), the emerging proteomics standards, and the middleware and computing resources of the EGEE Biomed VO grid infrastructure. In the setup, BioDCV is accessed by the MS-Analyzer workflow as a Web service, thus providing a complete grid environment for proteomics data analysis. Predictive classification studies on MALDI-TOF data based on this environment are presented.
Iris type:
1.1 Articolo in rivista
List of contributors:
Cannataro, M.; Barla, A.; Flor, R.; Jurman, G.; Merler, S.; Paoli, S.; Tradigo, G.; Veltri, P.; Furlanello, C.
Authors of the University:
TRADIGO GIUSEPPE
Handle:
https://iris.uniecampus.it/handle/11389/33720
Published in:
IEEE TRANSACTIONS ON NANOBIOSCIENCE
Journal
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-34250024292&doi=10.1109/TNB.2007.897495&partnerID=40&md5=64aaa3eb04a6a0a180681d4464a9eafe
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