A grid environment for high-throughput proteomics

被引：4
|
作者：
Cannataro, Mario [1 ]
Barla, Annalisa
Flor, Roberto
Jurman, Giuseppe
Merler, Stefano
Paoli, Silvano
Tradigo, Giuseppe
Veltri, Pierangelo
Furlanello, Cesare
机构：
[1] Magna Graecia Univ Catanzaro, Catanzaro, Italy
[2] Univ Genoa, DISI, I-16146 Genoa, Italy
[3] FBK Irst, I-38100 Trento, Italy
来源：
IEEE TRANSACTIONS ON NANOBIOSCIENCE | 2007年 / 6卷 / 02期
关键词：
biomarker discovery; feature ranking; grid problem solving environment; machine learning; mass spectrometry; ontology; predictive classification; proteomics; workflow;
D O I：
10.1109/TNB.2007.897495
中图分类号：
Q5 [生物化学];
学科分类号：
071010 ; 081704 ;
摘要：
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 an 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.
引用
收藏
页码：117 / 123
页数：7
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