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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Baker, P.G., Brass, A., Bechhofer, S., Goble, C., Paton, N., Stevens, R.: TAMBIS: Transparent Access to Multiple Bioinformatics Information Sources. An Overview. In: Proceedings of the Sixth International Conference on Intelligent Systems for Molecular Biology (ISMB 1998), Menlow Park, California, June 28-1 July, pp. 25–34. AAAI Press, Menlo Park (1998)
Cannataro, M., Comito, C.: A DataMining Ontology for Grid Programming. In: Workshop on Semantics in Peer-to-Peer and Grid Computing (in conj. with WWW2003), Budapest-Hungary (2003)
Cannataro, M., Comito, C., Guzzo, A., Veltri, P.: Integrating Ontology and Workflow in PROTEUS, a Grid-Based Problem Solving Environment for Bioinformatics. In: Proc. ITCC 2004 (Las Vegas, Nevada) (2004)
Cannataro, M., Comito, C., Lo Schiavo, F., Veltri, P.: Proteus, a Grid based Problem Solving Environment for Bioinformatics: Architecture and Experiments. IEEE Computational Intelligence Bulletin 3(1), 7–18 (2004)
Cannataro, M., Cuda, G., Gaspari, M., Guzzi, P.H., Mazza, T., Tagliaferri, P., Tradigo, G., Veltri, P., Costanzo, F., Venuta, S.: Mass Spectrometry Data Analysis for Early Detection of Inherited Breast Cancer. In: WIRN, CIBB Workshop (2004) (submitted paper)
Cannataro, M., Massara, A., Veltri, P.: The OnBrowser Ontology Manager: Managing Ontologies on the Grid. In: Int. Workshop on Semantic Intelligent Middleware for the Web and the Grid, in conjuntion with ECAI 2004, August 22-26, Springer, Heidelberg (2004)
Cuda, G., Cannataro, M., Quaresima, B., Baudi, F., Casadonte, R., Faniello, M.C., Tagliaferri, P., Veltri, P., Costanzo, F., Venuta, S.: Proteomic Profiling of Inherited Breast Cancer: Identification of Molecular Targets for Early Detection, Prognosis and Treatment, and Related Bioinformatics Tools. In: Apolloni, B., Marinaro, M., Tagliaferri, R. (eds.) WIRN 2003. LNCS, vol. 2859, pp. 245–257. Springer, Heidelberg (2003)
Foster, I., Kesselman, C.: The Grid: Blueprint for a Future Computing Infrastructure. Morgan Kaufmann Publishers, San Francisco (1999)
Fuellen, G.: A gentle guide to multiple alignment (1996)
Johnston, W.E.: Computational and Data Grids in Large-Scale Science and Engineering. Future Generation Computer Systems 18 (2002)
Kearney, P., Thibault, P.: Bioinformatics meets proteomics bridging the gap between mass spectrometry data analysis and cell biology. Journal of Bioinformatics and Computational Biology 1(1), 183–200 (2003)
Lilien, R.H., Farid, H., Donald, B.R.: Probabilistic disease classification of expression-dependent proteomic data from mass spectrometry of human serum. Journal of computational biology 10(6), 925–946 (2003)
Sender, K.: Dssp:dictionary of secondary structure prediction (1970)
Schweigert, S., Herde, P., Sibbald, R.: Issues in incorporation semantic integrity in molecular biological object-oriented databases. CABIOS 11(4), 339–347 (1995)
Selway, E.W., Walker, L., Riba-Garcia, J., Mohammed, I., Deery, S., Howard, M., Dunkley, J.A., Aebersold, T., Kell, R., Lilley, D.B., Roepstorff, K.S., Yates, P., Brass, J.R., Brown, A., Cach, A.J., Gaskell, P., Hubbard, S.J., Oliver Taylor, S.J., Paton, C.F., Garwood, N.W., Kirby, K.L., Stead, P.D., Yin, D.A., Deutsch, Z.: A systematic approach to modeling, capturing, and disseminating proteomics experimental data, WWW Document (2003), http://pedro.man.ac.uk/files/PEDRo_PowerPoint_talk.zip
Walker, D., Rana, O.F., Li, M., Shields, M.S., Huang, Y.: The Software Architecture of a Distributed Problem-Solving Environment. Concurrency: Practice and Experience 12(15) (2000)
Yeung, K.Y., Russo, W.L.: An empirical study on principal component analysis for clustering gene expression data, Uw-cse-2000-11-03, University of Washington (November 2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-540-30470-8_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23664-1
Online ISBN: 978-3-540-30470-8
eBook Packages: Springer Book Archive