PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A grid environment for high-throughput proteomics.
mario cannataro, annalisa barla, roberto flor, a gallo, giuseppe jurman, stefano merler, silvano paoli, g tradigo, p veltri and cesare furlanello
IEEE Trans. on Nanobiosciences Volume 2, Number 6, pp. 117-123, 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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:4114
Deposited By:annalisa barla
Deposited On:03 April 2008