Fuzzy-based Threshold Controlling Method for ART1 Clustering in GPCR Classification
2007, 12(6), pp.167-176
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science
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Fuzzy logic is used to represent qualitative knowledge and provides interpretability to a controlling system model in bioinformatics. This paper focuses on a bioinformatics data classification which is an important bioinformatics application. This paper reviews the two traditional controlling system models. The sequence-based threshold controller have problems of optimal range decision for threshold readjustment and long processing time for optimal threshold induction. And the binary-based threshold controller does not guarantee for early system stability in the GPCR data classification for optimal threshold induction. To solve these problems, we proposes a fuzzy-based threshold controller for ART1 clustering in GPCR classification. We implement the proposed method and measure processing time by changing an induction recognition success rate and a classification threshold value. And, we compares the proposed method with the sequence-based threshold controller and the binary-based threshold controller. The fuzzy-based threshold controller continuously readjusts threshold values with membership function of the previous recognition success rate. The fuzzy-based threshold controller keeps system stability and improves classification system efficiency in GPCR classification.
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[book] B. Ripley / 1996 / Pattern Recognition and Neural Networks / Cambridge Univ. Press
[book] H. H. Bothe / 1993 / Fuzzy Logic / Springer
[journal] C.T. Lin / 1994 / Reinforcement structure / parameter learning for neural network based fuzzy logic systems / IEEE Trans. Fuzzy Systems : 46~63
[book] S. Watson / 1994 / The G-protein Linked Receptor Facts Book / Academic Press
[journal] D.T. Chalmers / 2002 / The Use of Constitutively Active GPCRs in Drug Discovery and Functional Genomics / Nature Reviews, Drug Discovery 1 : 599~608
[confproc] P. Cheng / 2003 / Intelligent sequence adjusting algorithm based on general satisfaction function for air traffic arrival flow management / Computational Intelligence in Robotics and Automation, Proceedings : 533~537
[report] S. Gorinsky / 2002 / Extended Analysis of Binary Adjustment Algorithms / Department of Computer Sciences, The University of Texas at Austin
[journal] A. Devillez / 2002 / A fuzzy hybrid hierarchical clustering method with a new criterion able to find the optimal partition / Fuzzy Sets and Systems : 323~338
[confproc] D. Tran / 2002 / Generalised Fuzzy Hidden Markov Models for Speech Recognition / In Proceedings of the International Conference on Fuzzy Systems Calcutta
[confproc] D. Tran / 2002 / Fuzzy Clustering- Based Speaker Verification / Lecture Notes in Computer Science; Advances in Soft Computing - AFSS 2002 : 318~324
[journal] H. Takagi / 1992 / Neural networks designed on approximate reasoning architecture and there applications / IEEE Trans. Neural Networks : 752~760
[confproc] C.T. Lin / 2004 / Fuzzy Neural Network Classification Design Using Support Vector Machine / IEEE International Symposium on Circuits and Systems : 724~727
[confproc] G.A. Carpenter / 1988 / Adaptive resonance theory : Stable self-organization of neural recognition codes in response to arbitrary lists of input patterns / Proceedings of the 8th Conference of the Cognitive Science Society : 45~62
[book] J. S. R. Jang / 1997 / Neuro-Fuzzy and Soft Computing / Prentice-Hall International
[book] C. W. de silva / 1995 / Intelligent Control, In Fuzzy Logic Application / CRC
[journal] F. Horn / 1998 / Gpcrdb : an information system for g protein-coupled receptors / Nucleic Acids Res. 26 : 277~281
[journal] R. Karchin / 2002 / Classifying g-protein coupled receptors with support vector machines / Bioinformatics 18 : 147~159
[journal] K. . R. Sreekumar / 2004 / Predicting GPCR-G-Protein coupling using hidden Markov models / Bioinformatics : 3490~3499
[journal] Jaakkola / 1998 / Exploiting generative models in discriminative classifiers / In Advances in Neural Information Processing Systems / Morgan Kauffmann 11
[confproc] H. Ying / 2004 / Classifying G-protein Coupled Receptors with Support Vector Machine / Advances in Neural Network(ISNN 2004), LNCS : 448~452
[confproc] J. Cheetham / 2003 / Parallel clustal w for pc clusters / Proceedings of International Conference on Computational Science and Its Applications(ICCSA) 2668 : 300~309
[confproc] B. P Zeigler / 1996 / DEVS Framework for Modeling, Simulation, Analysis, and Design of Hybrid Systems / Hybrid II, Lecture Notes in Computer Science / Springer-Verlag : 529~551
[journal] J. Nutaro / 2007 / On the Stability and Performance of Discrete Event Methods for Simulating Continuous Systems / Journal of Computational Physics 227(1)
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