Data Mining in Proteomics Using Grid ComputingFotis Psomopoulos (Aristotle University of Thessaloniki, Greece) and Pericles Mitkas (Aristotle University of Thessaloniki, Greece)
Copyright © 2009. 23 pages.
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DOI: 10.4018/978-1-60566-374-6.ch013, ISBN13: 9781605663746, ISBN10: 1605663743, EISBN13: 9781605663753 Sample PDFCite Chapter
MLA
Psomopoulos, Fotis and Pericles Mitkas. "Data Mining in Proteomics Using Grid Computing." Handbook of Research on Computational Grid Technologies for Life Sciences, Biomedicine, and Healthcare. IGI Global, 2009. 245-267. Web. 23 May. 2012. doi:10.4018/978-1-60566-374-6.ch013
APA
Psomopoulos, F., & Mitkas, P. (2009). Data Mining in Proteomics Using Grid Computing. In M. Cannataro (Ed.), Handbook of Research on Computational Grid Technologies for Life Sciences, Biomedicine, and Healthcare (pp. 245-267). Hershey, PA: Medical Information Science Reference. doi:10.4018/978-1-60566-374-6.ch013
Chicago
Psomopoulos, Fotis and Pericles Mitkas. "Data Mining in Proteomics Using Grid Computing." In Handbook of Research on Computational Grid Technologies for Life Sciences, Biomedicine, and Healthcare, ed. Mario Cannataro, 245-267 (2009), accessed May 23, 2012. doi:10.4018/978-1-60566-374-6.ch013
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 | | TopAbstractThe scope of this chapter is the presentation of Data Mining techniques for knowledge extraction in proteomics, taking into account both the particular features of most proteomics issues (such as data retrieval and system complexity), and the opportunities and constraints found in a Grid environment. The chapter discusses the way new and potentially useful knowledge can be extracted from proteomics data, utilizing Grid resources in a transparent way. Protein classification is introduced as a current research issue in proteomics, which also demonstrates most of the domain – specific traits. An overview of common and custom-made Data Mining algorithms is provided, with emphasis on the specific needs of protein classification problems. A unified methodology is presented for complex Data Mining processes on the Grid, highlighting the different application types and the benefits and drawbacks in each case. Finally, the methodology is validated through real-world case studies, deployed over the EGEE grid environment. TopComplete Chapter List|
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| SHARE: A European Healthgrid Roadmap
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| Data Provenance in Scientific Workflows
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| Provenance Tracking and End-User Oriented Query Construction
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| Data Protection and Data Security Regarding Grid Computing in Biomedical Research
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6.
| Parallel, Distributed, and Grid-Based Data Mining: Algorithms, Systems, and Applications
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7.
| High Performance BLAST Over the Grid
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8.
| Functional Genomics Applications in GRID
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| PheGeeatHome: A Grid-Based Tool for Comparative Genomics
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10.
| High-Throughput GRID Computing for Life Sciences
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11.
| Management and Analysis of Mass Spectrometry Proteomics Data on the Grid
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12.
| High-Throughput Data Analysis of Proteomic Mass Spectra on the SwissBioGrid
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13.
| Data Mining in Proteomics Using Grid Computing
(pages 245-267)
Fotis Psomopoulos (Aristotle University of Thessaloniki, Greece), Pericles Mitkas (Aristotle University of Thessaloniki, Greece)
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14.
| ProGenGrid: A Grid Problem Solving for Bioinformatics
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15.
| A Graphical Workflow Modeler for Docking Process in Drug Discovery
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| BioSimGrid Biomolecular Simulation Database
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| Molecular Structure Determination on the Grid
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Russ Miller (Hauptman-Woodward Medical Research Institute and SUNY-Buffalo, USA), Charles Weeks (Hauptman-Woodward Medical Research Institute, USA)
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18.
| Aspects of Visualization and the Grid in a Biomedical Context
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19.
| Grid Analysis of Radiological Data
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20.
| Grid Computing in 3D Electron Microscopy Reconstruction
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| Hybrid Mock Circulatory System to Test Cardiovascular Prostheses on the Grid
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| Grid Technologies in Epidemiology
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| IntegraEPI: Epidemiologic Surveillance on the Grid
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26.
| Semantic Integration for Research Environments
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27.
| Virtual Laboratory for Collaborative Applications
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28.
| Leveraging the Power of the Grid with Opal
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31.
| A Grid Paradigm for e-Science Applications
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| Porting Applications to Grids and Clouds
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| Evaluating a Bio-Inspired Approach for the Design of a Grid Information System: The SO-Grid Portal
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35.
| Large-Scale Co-Phylogenetic Analysis on the Grid
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