title: A grid environment for high-throughput proteomics.
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.
authors: University Magna Graecia, Catanzaro, Italy. cannataro@unicz.it
authors: Mario
authors: M
authors: Cannataro
authors: Annalisa
authors: A
authors: Barla
authors: Roberto
authors: R
authors: Flor
authors: Giuseppe
authors: G
authors: Jurman
authors: Stefano
authors: S
authors: Merler
authors: Silvano
authors: S
authors: Paoli
authors: Giuseppe
authors: G
authors: Tradigo
authors: Pierangelo
authors: P
authors: Veltri
authors: Cesare
authors: C
authors: Furlanello
mesh_terms: D001185:Artificial Intelligence; D003628:Database Management Systems; D030562:Databases, Protein; D016247:Information Storage and Retrieval / Q000379:methods*; D020407:Internet; D013058:Mass Spectrometry / Q000379:methods; D020543:Proteome / Q000737:chemistry* / Q000145:classification / Q000502:physiology*; D040901:Proteomics / Q000379:methods*; D020539:Sequence Analysis, Protein; D012984:Software; D018511:Systems Integration; D014584:User-Computer Interface
chemical_list: D020543:Proteome