A novel strategy for gene selection of microarray data based on gene-to-class sensitivity information

PLoS ONE

Volumn 9, Issue 5, 2014, Pages

  Han, Fei ^a   Sun, Wei ^a   Ling, Qing Hua ^a,b  

^a JIANGSU UNIVERSITY   (China)

^b JIANGSU UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; DATA ANALYSIS; FILTRATION; GENE CLUSTER; GENE IDENTIFICATION; GENE TO CLASS SENSITIVITY INFORMATION; GENETIC PROCEDURES; GENETIC SCREENING; HYBRID GENE; HYBRID GENE SELECTION METHOD; MEDICAL INFORMATION; MICROARRAY ANALYSIS; PROCESS OPTIMIZATION; STATISTICAL ANALYSIS; STATISTICAL PARAMETERS; ANIMAL; ARABIDOPSIS; COMPUTER PROGRAM; DNA MICROARRAY; HUMAN; PROCEDURES;

ANIMALS; ARABIDOPSIS; HUMANS; NEURAL NETWORKS (COMPUTER); OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; SOFTWARE;

EID: 84901379660     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0097530     Document Type: Article

References (52)

1. Zhang JG, Deng HW (2007) Gene selection for classification of microarray data based on the Bayes error. BMC Bioinformatics 8: 370-378.
2. Huang HY, Liu CC, Zhou XH (2010) Bayesian approach to transforming public gene expression repositories into disease diagnosis databases. Proc. Natl. Acad. Sci. 107: 6823-6828.
3. Wang DF, Arapostathis A, Wilke CO, Markey MK (2012) Principal- Oscillation-Pattern analysis of gene expression. PLoS ONE 7: e28805.
4. Wong L (2013) An introduction to some new results in bioinformatics and computational biology. Journal of Bioinformatics and Computational Biology 11: 1301001.
5. Gutkin M, Shamir R, Dror G (2009) SlimPLS: A method for feature selection in gene expression-based disease classification. PLoS ONE 4: e6416.
6. Varma S, Simon R (2004) Iterative class discovery and feature selection using Minimal Spanning Trees. BMC Bioinformatics 5: 126-134.
7. Cao HB, Lei SF, Deng HW, Wang YP (2012) Identification of genes for complex diseases using integrated analysis of multiple types of genomic data. PLoS ONE 7: e42755.
8. Liu QZ, Sung AH, Chen ZX, Liu JZ, Huang XD, et al. (2009) Feature selection and classification of MAQC-II breast cancer and multiple myeloma microarray gene expression data. PLoS ONE 4: e8250.
9. Kohavi G, John R (1997) Wrappers for feature subset selection. Artificial Intelligence 97: 273-324.
10. Saeys Y, Inza I, Larranaga P (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23: 2507-2517. (Pubitemid 350048351)
11. Maldonado S, Weber R (2009) A wrapper method for feature selection using support vector machines. Information Sciences 179: 2208-2217.
12. Bermejo P, Gamez JA, Puerta JM (2011) A GRASP algorithm for fast hybrid (filter-wrapper) feature subset selection in high-dimensional datasets. Pattern Recognition 32: 701-711.
13. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning, Boston: Addison-Wesley Longman Publishing Co., Inc.
14. Kennedy J, Eberhart RC (1995) Particle swarm optimization. in Proc. IEEE International Conference on Neural Networks, 1942-1948.
15. Shi Y, Eberhart RC (1998) A modified particle swarm optimizer. in Proc. IEEE World Congress on Computational Intelligence, 69-73.
16. O'Hagan S, Knowles J, Kell DB (2012) Exploiting genomic knowledge in optimising molecular breeding programmes: Algorithms from evolutionary computing. PLoS ONE 7: e48862.
17. Chuang LY, Lin YD, Chang HW, Yang CH (2012) An Improved PSO algorithm for generating protective SNP barcodes in breast cancer. PLoS ONE 7: e37018.
18. Chuang LY, Huang HC, Lin MC, Yang CH (2011) Particle swarm optimization with reinforcement learning for the prediction of CpG islands in the human genome. PLoS ONE 6: e21036.
19. Yang CS, Chuang LY, Ke CH, Yang CH (2008) A hybrid feature selection method for microarray classification. International Journal of Computer Science 35: 285-290.
20. Shen Q, Shi WM, Kong W, Ye BX (2007) A combination of modified particle optimization algorithm and support vector machine for gene selection and tumor classification. Talanta 71: 1679-1683. (Pubitemid 46282844)
21. Li LP, Weinberg CR, Darden TA, Pedersen LG (2001) Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics 17: 1131-1142. (Pubitemid 33735339)
22. Saraswathi S, Sundaram S, Sundararajan N, Zimmermann M, Nilsen-Hamilton M (2011) ICGA-PSO-ELM approach for accurate multiclass cancer classification resulting in reduced gene sets in which genes encoding secreted proteins are highly represented. IEEE/ACM Transactions on Computational Biology and Bioinformatics 8: 452-463.
23. Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. in Proc. 1997 IEEE International Conference On Systems, Man, And Cybernetics, 4104-4108.
24. Werbos PJ (1974) Beyond Regression: New tools for prediction and analysis in the behavioral sciences. PhD thesis, Harvard University.
25. Cover TM, Hart PE (1967) Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13: 21-27.
26. Cortes C, Vapnik V (1995) Support-vector networks. Machine Learning 20: 273-297.
27. Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70: 489-501.
28. Yang SX, Han F, Guan J (2013) A hybrid gene selection and classification approach for microarray data based on clustering and PSO. Communications in Computer and Information Science 375: 88-93.
29. Zalik KR (2008) An efficient k-means clustering algorithm. Pattern Recognition Letters 29: 1385-1391.
30. Huang GB, Zhu QY, Siew CK (2004) Extreme learning machine: a new learning scheme of FNNs. in Proc. 2004 International Joint Conference on Neural Networks, 985-990.
31. Soria-Olivas E, Gómez-Sanchis J, Martín JD, Vila-Francés J, Martínez M, et al. (2011) BELM: Bayesian extreme learning machine. IEEE Transactions on Neural Networks 22: 505-509.
32. Han F, Huang DS (2006) Improved extreme learning machine for function approximation by encoding a priori information. Neurocomputing 69: 2369-2373. (Pubitemid 44340619)
33. Khan J, Wei JS, Ringner M, Saal LH, Ladanyi M, et al. (2001) Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Medicine 7: 637-679.
34. Han F, Ling QH, Huang DS (2008) Modified constrained learning algorithms incorporating additional functional constraints into neural networks. Information Sciences 178: 907-919. (Pubitemid 350050971)
35. Li YX, Ruan XG (2005) Feature selection for cancer classification based on support vector machine. Journal of Computer Research and Development 42: 1796-1801.
36. Ambroise C, McLachlan GJ (2002) Selection bias in gene extraction on the basis of microarray gene-expression data. Proc. Natl. Acad. Sci. 99: 6562-6566. (Pubitemid 34526172)
37. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, et al. (1999) Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286: 531-536.
38. Yang K, Cai Z, Li J, Lin G (2006) A stable gene selection in microarray data analysis. BMC Bioinformatics 7: 228-243.
39. Bhattacharjee A, Richards WG, Staunton J, Li C, Monti S, et al. (2001) Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc. Natl. Acad. Sci. 98: 13790-13795. (Pubitemid 33115977)
40. Tong DL (2009) Hybridising genetic algorithm-neural network (GANN) in marker genes detection. in Proc. The Eighth International Conference on Machine Learning and Cybernetics, 12-15.
41. Alon U, Barkai N, Notterman DA, Gish K, Ybarra S, et al. (1999) Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl. Acad. Sci. 96: 6745-6750. (Pubitemid 29274954)
42. Huang TM, Kecman V (2005) Gene extraction for cancer diagnosis by support vector machines-An improvement. Artificial Intelligence in Medicine 35: 185-194.
43. Chu F, Wang L (2005) Applications of support vector machines to cancer classification with microarray data. International Journal of Neural Systems 15: 475-484. (Pubitemid 44099482)
44. Wang XS, Simon R (2011) Microarray-based cancer prediction using single genes. BMC Bioinformatics, 12: 391.
45. Giugno R, Pulvirenti A, Cascione L, Pigola G, Ferro A (2013) MIDClass: Microarray data classification by association rules and gene expression intervals. PLoS ONE 8(8): e69873.
46. Oliveira PR, Romero RF (1996) A comparison between pca. and neural networks and the jpeg standard for performing image compression. in Proc. IEEE Workshop on. Cybernetic Vision, 112-116.
47. Li LP, Darden TA, Weinberg CR, Levine AJ, Pedersen LG (2001) Gene assessment and sample classification for gene expression data using a genetic algorithm/k-nearest neighbor method. Comb. Chem. High Throughput Screen, 4: 727-739. (Pubitemid 34003163)
48. Cho JH, Lee D, Park JH, Lee IB (2003) New gene selection for classification of cancer subtype considering within-class variation. FEBS Letters 551: 3-7. (Pubitemid 37281318)
49. Dudoit S, Fridlyand J, Speed TP (2002) Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association 97: 77-87.
50. Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Machine Learning 46: 389-422. (Pubitemid 34129977)
51. Ketchen DJ, Shook CL (1996) The application of cluster analysis in Strategic Management Research: An analysis and critique. Strategic Management Journal 17(6): 441-458.
52. Rousseuw PJ (1987) Silhouettes: a Graphical aid to the interpretation and validation of cluster analysis. Computational and Applied Mathematics 20: 53-65.