Abstract
Computational Grid technology has been noticed as an issue to solve large-scale bioinformatics-related problems and improves data accuracy and processing speed on multiple computation platforms with distributed bioDATA sets. This paper focuses on a GPCR data mining processing which is an important bioinformatics application. This paper proposes a Grid-based 3-tier ART1 classifier which operates an ART1 clustering data mining using grid computational resources with distributed GPCR data sets. This Grid-based 3-tier ART1 classifier is able to process a large-scale bioinformatics application in guaranteeing high bioDATA accuracy with reasonable processing resources. This paper evaluates performance of the Grid-based ART1 classifier in comparing to the ART1-based classifier and the ART1 optimum classifier. The data mining processing time of the Grid-based ART1 classifier is 18% data mining processing time of the ART1 optimum classifier and is the 12% data mining processing time of the ART1-based classifier. And we evaluate performance of the Grid-based 3-tier ART1 classifier in comparing to the Grid-based ART1 classifier. As data sets become larger, data mining processing time of the Grid-based 3-tier ART1 classifier more decrease than that of the Grid-based ART1 classifier. Computational Grid in bioinformatics applications gives a great promise of high performance processing with large-scale and geographically distributed bioDATA sets.
This research was supported by the MIC(Ministry of Information and Communication), Korea, under the ITRC(Information Technology Research Center) support program supervised by the IITA(Institute of Information Technology Assessment).
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
Watson, S., Arkinstall, S.: The G-protein Linked Receptor Facts Book. Academic Press, Burlington (1994)
Grossberg, S.: Neural Networks and Natural Intelligences, The definitive collection of papers from Grossberg’s group. MIT Bradford Press, Cambridge (1988)
Carpenter, G.A., Grossberg, S.: Adaptive resonance theory: Stable self-organization of neural recognition codes in response to arbitrary lists of input patterns. In: Proceedings of the 8th Conference of the Cognitive Science Society, pp. 45–62. Erlbaum Associates, Hillsdale (1988)
Foster, I., Kesselman, C.: The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (1998)
Fumikazu, K., Hiroyuki, U., Kenji, S., Akihiko, K.: A network design for Open Bioinformatics Grid (GBIGrid). In: Proc. The 3rd Annual Meeting, Chem-Bio Informatics Society, pp. 192–193 (2002)
Stevens, R.D., Robinson, A.J., Goble, C.A.: myGrid: personalised bioinformatics on the information grid. Bioinformatics, 302–304 (2003)
Karo, M., Dwan, C., Freeman, J., Weissman, J., Livny, M., Retzel, E.: Applying Grid technologies to bioinformatics. In: 10th IEEE International Symposium on High Performance Distributed Computing, pp. 441–442 (2001)
Li, K.B.: ClustalW-MPI: ClustalW Analysis Using Distributed and Parallel Computing. Bioinformatics 19, 1585–1586 (2003)
Baxt, W.G.: Application of neural networks to clinical medicine. Lancet 346, 1135–1138 (1995)
Finne, P., Finne, R., Stenman, U.H.: Neural network analysis of clinicopathological factorss in urological disease: a critical evaluation of available techniques. BJU Int. 88, 825–831 (2001)
Lin, J.S., Ligomenides, P.A., Freedman, M.T., et al.: Application of artificial neural networks for reduction of false-positive detections in digital chest radiographs. In: Proc. Annu. Symp. Comput. Appl. Med. Care, pp. 434–438 (1993)
Wu, Y.C., Doi, K., Giger, M.L., et al.: Reduction of false positives in computerized detection of lung ndodules in chest radiographs using artificial neural networks, discriminant analysis, and a rule-based scheme. J. Digit Imaging 7, 196–207 (1994)
Biganzoli, E., Boracchi, P., Mariani, L., et al.: Feed forward neural networks for the analysis of censored survival data: a partial logistic regression approach. Stat. Med. 17, 1169–1186 (1998)
Goldberg, D.E.: Genetic algorithms in search, optimization and machine learning, 1st edn. Addison-Welsey, Reading (1989)
Jefferson, M.F., Narayanan, M.N., Lucas, S.B.: A neural network computer method to model the INR response of individual patients anticoagulated with warfarin. Br. J. Haematol. 89(1), 29 (1995)
Noguch, H., Hanai, T., Honda, H., Harrison, L.C.M., Kobayashi, T.: Fuzzy neural network-based prediction of the motif for MHC class II binding peptides. J. Biosci. Bioeng. 92, 227–231 (2001)
Kapolka, A.: The Extensible Run-Time Infrastructure (XRTI): An Experimental Implementation of Proposed Improvements to the High Level Architecture. Master’s Thesis, Naval Postgraduate School (2003)
Defense Modeling and Simulation Office (DMSO). Runtime Infrastructure (RTI) (2003), http://www.dmso.mil/public/transition/hla/rti/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cho, K.C., Park, D.H., Lee, J.S. (2006). Computational Grid-Based 3-tier ART1 Data Mining for Bioinformatics Applications. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2006. Lecture Notes in Computer Science(), vol 4223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881599_60
Download citation
DOI: https://doi.org/10.1007/11881599_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45916-3
Online ISBN: 978-3-540-45917-0
eBook Packages: Computer ScienceComputer Science (R0)