Abstract
Novel experiments in bio-medical domain involve different technologies such as mass spectrometry, bio-molecular profiling, nanotechnology, drug design, and of course bioinformatics. Bioinformatics platforms should support both modelling of experiments, and collection, storing and analysis of the produced data. The advent of proteomics has brought with it the hope of discovering novel biomarkers that can be used for early detection, prognosis and treatment of diseases. Mass Spectrometry (MS) is widely used for the mass spectral identification of the thousands of proteins that populate complex biosystems such as serum and tissue. Data Mining (DM) is the semi-automated extraction of patterns representing knowledge implicitly stored in large databases. The combined use of MS with DM is a novel approach in proteomic pattern analysis and is emerging as an effective method for the early diagnosis of diseases. However, it involves large data storage and computing power so it is natural to consider Grid as a reference environment. The paper presents how PROTEUS, a Grid-based Problem Solving Environment for bioinformatics applications, allows formulating, modelling and designing of proteomics experiments involving DM analysis of MS data.
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Cannataro, M., Guzzi, P.H., Mazza, T., Veltri, P. (2004). Using PROTEUS for Modeling Data Mining Analysis of Proteomics Experiments on the Grid. In: Meersman, R., Tari, Z., Corsaro, A. (eds) On the Move to Meaningful Internet Systems 2004: OTM 2004 Workshops. OTM 2004. Lecture Notes in Computer Science, vol 3292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30470-8_40
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DOI: https://doi.org/10.1007/978-3-540-30470-8_40
Publisher Name: Springer, Berlin, Heidelberg
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