A kernel-based multivariate feature selection method for microarray data classification

PLoS ONE

Volumn 9, Issue 7, 2014, Pages

  Sun, Shiquan ^a   Peng, Qinke ^a   Shakoor, Adnan ^a  

^a XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CANCER CLASSIFICATION; DISCRIMINANT ANALYSIS; FISHERS LINEAR DISCRIMINANT ANALYSIS; GENE EXPRESSION; GENE INTERACTION; K NEAREST NEIGHBOR; KERNEL METHOD; MICROARRAY ANALYSIS; MULTIVARIATE ANALYSIS; MULTIVARIATE FEATURE SELECTION; NUCLEOTIDE SEQUENCE; PARTIAL LEAST SQUARES REGRESSION; RADIAL BASED FUNCTION; SUPPORT VECTOR MACHINE; ALGORITHM; ARTIFICIAL INTELLIGENCE; COMPUTER PROGRAM; DNA MICROARRAY; GENETICS; HUMAN; NEOPLASM; PROCEDURES; REGRESSION ANALYSIS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; HUMANS; LEAST-SQUARES ANALYSIS; NEOPLASMS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; SOFTWARE;

EID: 84904543846     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0102541     Document Type: Article

References (44)

1. Piao Y, Piao M, Park K, Ryu KH (2012) An ensemble correlation-based gene selection algorithm for cancer classification with gene expression data. Bioinformatics 28: 3306-3315.
2. Kohavi R, John GH (1997) Wrappers for feature subset selection. Artificial Intelligence 97: 273-324. (Pubitemid 127401107)
3. Xiong M, Fang X, Zhao J (2001) Biomarker identification by feature wrappers. Genome Research 11: 1878-1887. (Pubitemid 33100429)
4. Senthamarai Kannan S, Ramaraj N (2010) A novel hybrid feature selection via symmetrical uncertainty ranking based local memetic search algorithm. Knowledge-Based Systems 23: 580-585.
5. Díaz-Uriarte R, Andrés SAd (2006) Gene selection and classification of microarray data using random forest. BMC Bioinformatics 7.
6. Lei Y, Huan L (2004) Efficient feature selection via analysis of relevance and redundancy. Journal of Machine Learning Research 5: 1205-1224.
7. Peng H, Fulmi L, Ding C (2005) Feature selection based on mutual information criteria of maxdependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 27: 1226-1238. (Pubitemid 41245053)
8. Saeys Y, Inza I, Larranaga P (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23: 2507-2517. (Pubitemid 350048351)
9. Gavin B, Adam P, Ming-Jie Z, Mikel L (2012) Conditional likelihood maximisation: a unifying framework for information theoretic feature selection. Journal of Machine Learning Research 13: 27-66.
10. Isabelle G, Andr Elisseeff (2003) An introduction to variable and feature selection. The Journal of Machine Learning Research 3: 1157-1182.
11. Balagani KS, Phoha VV (2010) On the feature selection criterion based on an approximation of multidimensional mutual information. IEEE Transactions on Pattern Analysis and Machine Intelligence 32: 1342-1343.
12. De Jay N, Papillon-Cavanagh S, Olsen C, El-Hachem N, Bontempi G, et al. (2013) mrmre: an r package for parallelized mrmr ensemble feature selection. Bioinformatics 29: 2365-2368.
13. Yeoh EJ, Ross ME, Shurtleff SA, Williams WK, Patel D, et al. (2002) Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. Cancer Cell 1: 133-143. (Pubitemid 41039160)
14. Gevaert O, Smet FD, Timmerman D, Moreau Y, Moor BD (2006) Predicting the prognosis of breast cancer by integrating clinical and microarray data with bayesian networks. Bioinformatics 22: e184-e190. (Pubitemid 44288285)
15. Sun X, Liu Y, Li J, Zhu J, Chen H, et al. (2012) Feature evaluation and selection with cooperative game theory. Pattern Recognition 45: 2992-3002.
16. Wold S, Ruhe A, Wold H, Dunn IW (1984) The collinearity problem in linear regression. the partial least squares (pls) approach to generalized inverses. SIAM Journal of Scientific and Statistical Computations 5: 735-743.
17. Gutkin M, Shamir R, Dror G (2009) Slimpls: a method for feature selection in gene expression-based disease classification. Plos One 4.
18. You W, Yang Z, Ji G (2014) Pls-based recursive feature elimination for high-dimensional small sample. Knowledge-Based Systems 55: 15-28.
19. You W, Yang Z, Ji G (2014) Feature selection for high-dimensional multicategory data using pls-based local recursive feature elimination. Expert Systems with Applications 41: 1463-1475.
20. You W, Yang Z, Yuan M, Ji G (2014) Totalpls: Local dimension reduction for multicategory microarray data. IEEE Transactions on Human-Machine Systems 44: 125-138.
21. Wold H (1966) Estimation of principal components and related models by iterative least squares. Multivariate Analysis. New York: Academic.
22. Shawe-Taylor J, Nello C (2004) Kernel methods for pattern analysis. UK: Cambridge University.
23. Ránnar S, Lindgren F, Geladi P, Wold S (1994) A pls kernel algorithm for data sets with many variables and fewer objects. part 1: Theory and algorithm. Journal of Chemometrics 8: 111-125.
24. Wold S, Johansson W, Cocchi M (1993) PLS-partial least-squares projections to latent structures. 3D QSAR in Drug Design, Theory Methods and Applications. Berlin: Springer-Verlag.
25. Ji G, Yang Z, You W (2011) Pls-based gene selection and identification of tumor-specific genes. IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Reviews 41: 830-841.
26. Li GZ, Zhao RW, Qu HN, You M (2012) Model selection for partial least squares based dimension reduction. Pattern Recognition Letters 33: 524-529.
27. 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: 531-537.
28. Wang HQ, Wong HS, Huang DS, Shu J (2007) Extracting gene regulation information for cancer classification. Pattern Recognition 40: 3379-3392.
29. Gordon GJ, Jensen RV, Hsiao LL, Gullans SR, Blumenstock JE, et al. (2002) Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma. Cancer Research 62: 4963-4967. (Pubitemid 34984420)
30. Singh D, Febbo PG, Ross K, Jackson DG, Manola J, et al. (2002) Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1: 203-209. (Pubitemid 41039166)
31. Shipp MA, Ross KN, Tamayo P, Weng AP, Kutok JL, et al. (2002) Diffuse large b-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nature Medicine 8: 68-74. (Pubitemid 34101731)
32. Pomeroy SL, Tamayo P, Gaasenbeek M, Sturla LM, Angelo M, et al. (2002) Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature 415: 436-442. (Pubitemid 34100955)
33. Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, et al. (2000) Distinct types of diffuse large b-cell lymphoma identified by gene expression profiling. Nature 403: 503-511. (Pubitemid 30082188)
34. 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: 673-679. (Pubitemid 32588022)
35. Armstrong SA, Staunton JE, Silverman LB, Pieters R, de Boer ML, et al. (2002) Mll translocations specify a distinct gene expression profile that distinguishes a unique leukemia. Nature Genetics 30: 41-47.
36. Yang K, Cai ZP, Li JZ, Lin GH (2006) A stable gene selection in microarray data analysis. BMC Bioinformatics 7.
37. 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. Proceedings Of the National Academy Of Sciences Of the United States Of America 98: 13790-13795. (Pubitemid 33115977)
38. Dramiski M, Rada-Iglesias A, Enroth S, Wadelius C, Koronacki J, et al. (2008) Monte carlo feature selection for supervised classification. Bioinformatics 24: 110-117.
39. Brown G, Pocock A, Zhao MJ, Lujn M (2012) Conditional likelihood maximisation: a unifying framework for information theoretic feature selection. The Journal of Machine Learning Research 13: 27-66.
40. Shi P, Ray S, Zhu Q, Kon MA (2011) Top scoring pairs for feature selection in machine learning and applications to cancer outcome prediction. Bmc Bioinformatics 12.
41. Nanni L, Brahnam S, Lumini A (2012) Combining multiple approaches for gene microarray classification. Bioinformatics 28: 1151-1157.
42. Wang Y, Klijn JGM, Zhang Y, Sieuwerts AM, Look MP, et al. (2005) Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. The Lancet 365: 671-679. (Pubitemid 40260888)
43. Chu W, Ghahramani Z, Falciani F, Wild DL (2005) Biomarker discovery in microarray gene expression data with gaussian processes. Bioinformatics 21: 3385-3393. (Pubitemid 41222443)
44. Lee KE, Sha N, Dougherty ER, Vannucci M, Mallick BK (2003) Gene selection: a bayesian variable selection approach. Bioinformatics 19: 90-97. (Pubitemid 36150185)