Modified AHP for Gene Selection and Cancer Classification Using Type-2 Fuzzy Logic

IEEE Transactions on Fuzzy Systems

Volumn 24, Issue 2, 2016, Pages 273-287

  Nguyen, Thanh ^a   Nahavandi, Saeid ^a  

^a DEAKIN UNIVERSITY   (Australia)

Author keywords

analytic hierarchy process (AHP); fuzzy c means clustering; gene expression levels; gene selection; interval type 2 fuzzy logic; microarray data classification

Indexed keywords

ANALYTIC HIERARCHY PROCESS; ARTIFICIAL INTELLIGENCE; COMPUTER CIRCUITS; DECISION SUPPORT SYSTEMS; DISEASES; FUZZY SYSTEMS; GENE EXPRESSION; GENES; HIERARCHICAL SYSTEMS; RECONFIGURABLE HARDWARE; SIGNAL RECEIVERS; SIGNAL TO NOISE RATIO; STATISTICAL METHODS;

ANALYTIC HIERARCHY PROCESS (AHP); FUZZY C MEANS CLUSTERING; GENE EXPRESSION LEVELS; GENE SELECTION; INTERVAL TYPE-2 FUZZY LOGIC; MICROARRAY DATA;

FUZZY LOGIC;

EID: 84963804075     PISSN: 10636706     EISSN: None     Source Type: Journal    
DOI: 10.1109/TFUZZ.2015.2453153     Document Type: Article

References (48)

1. O. Dagliyan, F. Uney-Yuksektepe, I. H. Kavakli, and M. Turkay, "Optimization based tumor classification from microarray gene expression data," PLoS One, vol. 6, no. 2, p. e14579, 2011.
2. K. E. Basford, G. J. McLachlan, and S. I. Rathnayake, "On the classification of microarray gene-expression data," Briefings Bioinformat., vol. 14, no. 4, pp. 402-410, 2013.
3. H. Yu, J. Ni, and J. Zhao, "ACOSampling: An ant colony optimizationbased undersampling method for classifying imbalanced DNA microarray data," Neurocomputing, vol. 101, pp. 309-318, 2013.
4. R. Giugno, A. Pulvirenti, L. Cascione, G. Pigola, and A. Ferro, "MIDClass: Microarray data classification by association rules and gene expression intervals," PloS One, vol. 8, no. 8, p. e69873, 2013.
5. M. Reboiro-Jato, J. P. Arrais, J. L. Oliveira, and F. Fdez-Riverola, "GeneCommittee: A web-based tool for extensively testing the discriminatory power of biologically relevant gene sets in microarray data classification," BMC Bioinformat., vol. 15, no. 1, p. 31, 2014.
6. M. Reboiro-Jato, F. Díaz, D. Glez-Peña, and F. Fdez-Riverola, "A novel ensemble of classifiers that use biological relevant gene sets formicroarray classification," Appl. Soft Comput., vol. 17, pp. 117-126, 2014.
7. X. Cai, J. Wei, G. Wen, and Z. Yu, "Local and global preserving semisupervised dimensionality reduction based on random subspace for cancer classification," IEEE J. Biomed. Health Informat., vol. 18, no. 2, pp. 500-507, Mar. 2014.
8. J. A. Sanz, M. Galar, A. Jurio, A. Brugos, M. Pagola, and H. Bustince, "Medical diagnosis of cardiovascular diseases using an interval-valued fuzzy rule-based classification system," Appl. Soft Comput., vol. 20, pp. 103-111, 2013.
9. W. L. Chen, C. D. Kan, C. H. Lin, and T. Chen, "A rule-based decisionmaking diagnosis system to evaluate arteriovenous shunt stenosis for hemodialysis treatment of patients using fuzzy petri nets," IEEE J. Biomed. Health Informat., vol. 18, no. 2, pp. 703-713, Mar. 2014.
10. C. Lin, Y. Hou, T. Y. Chen, and K. H. Chen, "Breast nodules computeraided diagnostic system design using fuzzy cerebellar model neural networks," IEEE Trans. Fuzzy Syst., vol. 22, no. 3, pp. 693-699, Jun. 2014.
11. E. Huerta, B. Duval, and J. K. Hao, "Fuzzy logic for elimination of redundant information of microarray data," Genomics, Proteomics Bioinformatics, vol. 6, no. 2, pp. 61-73, 2008.
12. P. Maji, "Fuzzy-rough supervised attribute clustering algorithm and classification ofmicroarray data," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 41, no. 1, pp. 222-233, Feb. 2011.
13. M. Khashei, A. Zeinal Hamadani, and M. Bijari, "A fuzzy intelligent approach to the classification problem in gene expression data analysis," Knowl.-Based Syst., vol. 27, pp. 465-474, 2012.
14. P. Ganesh Kumar, T. Aruldoss Albert Victoire, P. Renukadevi, and D. Devaraj, "Design of fuzzy expert system for microarray data classification using a novel genetic swarm algorithm," Expert Syst. Appl., 39, no. 2, pp. 1811-1821, 2012.
15. J. M. Mendel, "Type-2 fuzzy sets and systems: An overview," IEEE Comput. Intell. Mag., vol. 2, no. 1, pp. 20-29, Feb. 2007.
16. T. L. Saaty and K. Peniwati, Group Decision Making: Drawing Out and Reconciling Differences. Pittsburgh, PA, USA: RWS Publications, 2013.
17. Z. Wang, V. Palade, and Y. Xu, "Neuro-fuzzy ensemble approach for microarray cancer gene expression data analysis," in Proc. 2nd Int. Symp. Evolving Fuzzy Syst., 2006, pp. 241-246.
18. K. Y. Yeung, R. E. Bumgarner, and A. E. Raftery, "Bayesian model averaging: development of an improved multi-class, gene selection and classification tool for microarray data," Bioinformatics, vol. 21, no. 10, pp. 2394-2402, 2005.
19. J. H. Hong and S. B. Cho, "Gene boosting for cancer classification based on gene expression profiles," Pattern Recog., vol. 42, no. 9, pp. 1761-1767, 2009.
20. L. Deng, J. Pei, J. Ma, and D. L. Lee, "A rank sum test method for informative gene discovery," in Proc. 10th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2004, pp. 410-419.
21. T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov, and E. S. Lander, "Molecular classification of cancer: class discovery and class prediction by gene expression monitoring," Science, vol. 286, no. 5439, pp. 531-537, 1999.
22. T. Nguyen and L. Gordon-Brown, "Constrained fuzzy hierarchical analysis for portfolio selection under higher moments," IEEE Trans. Fuzzy Syst., vol. 20, no. 4, pp. 666-682, Aug. 2012.
23. N. N. Karnik and J. M. Mendel, "Operations on type-2 fuzzy sets," Fuzzy Sets Syst., vol. 122, no. 2, pp. 327-348, 2001.
24. R. John and S. Coupland, "Type-2 fuzzy logic: A historical view," IEEE Comput. Intell. Mag., vol. 2, no. 1, pp. 57-62, Feb. 2007.
25. P. Melin and O. Castillo, "A review on type-2 fuzzy logic applications in clustering, classification and pattern recognition," Appl. Soft Comput., vol. 21, pp. 568-577, 2014.
26. P. Melin, L. Astudillo, O. Castillo, F. Valdez, and M. Garcia, "Optimal design of type-2 and type-1 fuzzy tracking controllers for autonomous mobile robots under perturbed torques using a new chemical optimization paradigm," Exp. Syst. Appl., vol. 40, no. 8, pp. 3185-3195, 2013.
27. J. M. Mendel, R. I. John, and F. Liu, "Interval type-2 fuzzy logic systems made simple," IEEE Trans. Fuzzy Syst., vol. 14, no. 6, pp. 808-821, Dec. 2006.
28. J. Mendel and X. Liu, "Simplified interval type-2 fuzzy logic systems," IEEE Trans. Fuzzy Syst., vol. 21, no. 6, pp. 1056-1069, Dec. 2013.
29. J. C. Cortes-Rios, E. Gomez-Ramirez, H. A. Ortiz-De-La-Vega, O. Castillo, and P. Melin, "Optimal design of interval type 2 fuzzy controllers based on a simple tuning algorithm," Appl. Soft Comput., vol. 23, pp. 270-285, 2014.
30. O. Castillo and P. Melin, "A review on interval type-2 fuzzy logic applications in intelligent control," Inf. Sci., vol. 279, pp. 615-631, 2014.
31. N. N. Karnik, J. M. Mendel, and Q. Liang, "Type-2 fuzzy logic systems," IEEE Trans. Fuzzy Syst., vol. 7, no. 6, pp. 643-658, Dec. 1999.
32. T. Nguyen, A. Khosravi, S. Nahavandi, and D. Creighton, "Neural network and interval type-2 fuzzy system for stock price forecasting," in Proc. IEEE Int. Conf. Fuzzy Syst., 2013, pp. 1-8.
33. D. Wu and J. M. Mendel, "Enhanced Karnik-Mendel algorithms," IEEE Trans. Fuzzy Syst., vol. 17, no. 4, pp. 923-934, Aug. 2009.
34. C. Y. Yeh, W. H. Jeng, and S. J. Lee, "An enhanced type-reduction algorithm for type-2 fuzzy sets," IEEE Trans. Fuzzy Syst., 19, no. 2, pp. 227-240, Apr. 2011.
35. D. Wu and M. Nie, "Comparison and practical implementation of typereduction algorithms for type-2 fuzzy sets and systems," in Proc. IEEE Int. Conf. Fuzzy Syst., Taipei, Taiwan, 2011, pp. 2131-2138.
36. K. Duran, H. Bernal, and M. Melgarejo, "Improved iterative algorithm for computing the generalized centroid of an interval type-2 fuzzy set," in Proc. North Am. Fuzzy Inf. Process. Soc., New York, NY, USA, 2008, pp. 1-5.
37. J. C. Bezdek, R. Ehrlich, and W. Full, "FCM: The fuzzy c-means clustering algorithm," Comput. Geosci., vol. 10, no. 2, pp. 191-203, 1984.
38. J. Khan, J. S. Wei, M. Ringner, L. H. Saal, M. Ladanyi, F. Westermann, and P. S. Meltzer, "Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks," Nature Med., vol. 7, no. 6, pp. 673-679, 2001.
39. C. Ding and H. Peng, "Minimum redundancy feature selection from microarray gene expression data," J. Bioinformat. Comput. Biol., vol. 3, no. 2, pp. 185-205, 2005.
40. S. Monti, K. J. Savage, and J. K. Kutok, "Molecular profiling of diffuse large B cell lymphoma reveals a novel disease subtype with brisk host inflammatory response and distinct genetic features," Blood, vol. 105, no. 5, pp. 1851-1861, 2005.
41. T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov, and E. S. Lander, "Molecular classification of cancer: class discovery and class prediction by gene expression monitoring," Science, vol. 286, no. 5439, pp. 531-537, 1999.
42. D. Singh, P. G. Febbo, K. Ross, D. G. Jackson, J. Manola, C. Ladd, and W. R. Sellers, "Gene expression correlates of clinical prostate cancer behavior," Cancer Cell, vol. 1, no. 2, pp. 203-209, 2002.
43. K. Hornik, M. Stinchcombe, and H. White, "Multilayer feedforward networks are universal approximators," Neural Netw., vol. 2, no. 5, pp. 359-366, 1989.
44. C. Cortes and V. Vapnik, "Support-vector networks," Mach. Learning, vol. 20, no. 3, pp. 273-297, 1995.
45. G. A. Carpenter, S. Grossberg, N. Markuzon, J. H. Reynolds, and D. B. Rosen, "Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps," IEEE Trans. Neural Netw., vol. 3, no. 5, pp. 698-713, Sep. 1992.
46. G. A. Carpenter, "Default ARTMAP," in Proc. Int. Joint Conf. Neural Netw., 2003, vol. 2, pp. 1396-1401.
47. I. S. Kohane, A. T. Kho, and A. J. Butte, Microarrays for an Integrative Genomics. Cambridge, MA, USA: MIT Press, 2003.
48. B. M. Bolstad, R. A. Irizarry, M. Åstrand, and T. P. Speed, "A comparison of normalization methods for high density oligonucleotide array data based on variance and bias," Bioinformatics, vol. 19, no. 2, pp. 185-193, 2003.