On Efficient Feature Ranking Methods for High-Throughput Data Analysis
Authors: 
Bo Liao, Yan Jiang, Wei Liang, Lihong Peng, Li Peng, Damien Hanyurwimfura, Zejun Li, Min ChenAuthors Info & Claims
Pages 1374 - 1384
Abstract
Efficient mining of high-throughput data has become one of the popular themes in the big data era. Existing biology-related feature ranking methods mainly focus on statistical and annotation information. In this study, two efficient feature ranking methods are presented. Multi-target regression and graph embedding are incorporated in an optimization framework, and feature ranking is achieved by introducing structured sparsity norm. Unlike existing methods, the presented methods have two advantages: (1) the feature subset simultaneously account for global margin information as well as locality manifold information. Consequently, both global and locality information are considered. (2) Features are selected by batch rather than individually in the algorithm framework. Thus, the interactions between features are considered and the optimal feature subset can be guaranteed. In addition, this study presents a theoretical justification. Empirical experiments demonstrate the effectiveness and efficiency of the two algorithms in comparison with some state-of-the-art feature ranking methods through a set of real-world gene expression data sets.
References
[1]
T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov, H. Coller, M. L. Loh, J. R. Downing, M. A. Caligiuri, C. D. Bloomfield, 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.
[2]
J. E. Elias and S. P. Gygi, "Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry," Nature Methods, vol. 4, no. 3, pp. 207-214, 2007.
[3]
Y. Nannya, M. Sanada, K. Nakazaki, N. Hosoya, L. Wang, A. Hangaishi, M. Kurokawa, S. Chiba, D. K. Bailey, G. C. Kennedy, and S. Ogawa, "A robust algorithm for copy number detection using high-density oligonucleotide single nucleotide polymorphism genotyping arrays," Cancer Res., vol. 65, no. 14, pp. 6071-6079, 2005.
[4]
S. A. Armstrong, J. E. Staunton, L. B. Silverman, R. Pieters, M. L. den Boer, M. D. Minden, S. E. Sallan, E. S. Lander, T. R. Golub, and S. J. Korsmeyer, "Mll translocations specify a distinct gene expression profile that distinguishes a unique leukemia," Nature Genetics, vol. 30, no. 1, pp. 41-47, 2001.
[5]
J. Bernardo, M. Bayarri, J. Berger, A. Dawid, D. Heckerman, A. Smith, and M. West, "Bayesian factor regression models in the large p, small n paradigm," Bayesian Statist., vol. 7, pp. 733-742, 2003.
[6]
X. He and P. Niyogi, "Locality preserving projections," in Proc. Neural Inform. Process. Syst. 16, 2003, vol. 16, pp. 234-241.
[7]
B. Liao, Y. Jiang, W. Liang, W. Zhu, L. Cai, and Z. Cao, "Gene selection using locality sensitive Laplacian score," IEEE/ACM Trans. Comput. Biol. Bioinf., vol. 11, no. 6, pp. 1146-1156, Nov. 2014.
[8]
R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification. Hoboken, NJ, USA: Wiley, 2012.
[9]
X. He, D. Cai, and P. Niyogi, "Laplacian score for feature selection," in Proc. Adv. Neural Inform. Process. Syst., 2005, pp. 507-514.
[10]
S. Bandyopadhyay, S. Mallik, and A. Mukhopadhyay, "A survey and comparative study of statistical tests for identifying differential expression from microarray data," IEEE/ACM Trans. Comput. Biol. Bioinf., vol. 11, no. 1, pp. 95-115, Jan. 2014.
[11]
C. Lazar, J. Taminau, S. Meganck, D. Steenhoff, A. Coletta, C. Molter, V. de Schaetzen, R. Duque, H. Bersini, and A. Nowe, "A survey on filter techniques for feature selection in gene expression microarray analysis," IEEE/ACM Trans. Comput. Biol. Bioinf., vol. 9, no. 4, pp. 1106-1119, Jul.-Aug. 2012.
[12]
S. Niijima and Y. Okuno, "Laplacian linear discriminant analysis approach to unsupervised feature selection," IEEE/ACM Trans. Comput. Biol. Bioinf., vol. 6, no. 4, pp. 605-614, Oct. 2009.
[13]
D. Cai, C. Zhang, and X. He, "Unsupervised feature selection for multi-cluster data," in Proc. 16th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2010, pp. 333-342.
[14]
F. Nie, S. Xiang, Y. Jia, C. Zhang, and S. Yan, "Trace ratio criterion for feature selection," in Proc. 23rd Nat. Conf. Artif. Intell., 2008, vol. 2, pp. 671-676.
[15]
L.-K. Luo, D.-F. Huang, L.-J. Ye, Q.-F. Zhou, G.-F. Shao, and H. Peng, "Improving the computational efficiency of recursive cluster elimination for gene selection," IEEE/ACM Trans. Comput. Biol. Bioinf., vol. 8, no. 1, pp. 122-129, Jan. 2011.
[16]
P. A. Mundra and J. C. Rajapakse, "SVM-RFE with MRMR filter for gene selection," IEEE Trans. NanoBiosci., vol. 9, no. 1, pp. 31- 37, Mar. 2010.
[17]
C. Ding, D. Zhou, X. He, and H. Zha, "R 1-PCA: Rotational invariant l 1-norm principal component analysis for robust subspace factorization," in Proc. 23rd Int. Conf. Mach. Learn., 2006, pp. 281-288.
[18]
S. Xiang, F. Nie, G. Meng, C. Pan, and C. Zhang, "Discriminative least squares regression for multiclass classification and feature selection," IEEE Trans. Neural Netw. Learn. Syst., vol. 23, no. 11, pp. 1738-1754, Nov. 2012.
[19]
X. Niyogi, "Locality preserving projections," in Proc. Neural Inform. Process. Syst., 2004, vol. 16, p. 153.
[20]
J. Friedman, T. Hastie, and R. Tibshirani, "A note on the group lasso and a sparse group lasso," arXiv preprint arXiv:1001.0736, 2010.
[21]
D. Kong, C. Ding, and H. Huang, "Robust nonnegative matrix factorization using l21-norm," in Proc. 20th ACM Int. Conf. Inform. Knowl. Manage., 2011, pp. 673-682.
[22]
S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge, U.K.: Cambridge Univ. Press, 2009.
[23]
F. Nie, H. Huang, X. Cai, and C. Ding, "Efficient and robust feature selection via joint l2, 1-norms minimization," in Proc. Adv. Neural Inf. Process. Syst., 2010, vol. 23, pp. 1813-1821.
[24]
K. B. Petersen and M. S. Pedersen, "The matrix cookbook," Tech. Univ. Denmark, pp. 7-15, 2008.
[25]
[Online]. Available: http://levis.tongji.edu.cn/gzli/data/mirrorkentridge.html, Jul. 2013.
[26]
S. L. Pomeroy, P. Tamayo, M. Gaasenbeek, L. M. Sturla, M. Angelo, M. E. McLaughlin, J. Y. Kim, L. C. Goumnerova, P. M. Black, C. Lau, C. A. Jeffrey, Z. David, M. O. James, C. Tom, W. Cynthia, A. B. Jaclyn, P. Tomaso, M. Shayan, R. Ryan, C. Andrea, S. Gustavo, N. L. David, P. M. Jill, S. L. Eric, and R. G. Todd, "Prediction of central nervous system embryonal tumour outcome based on gene expression," Nature, vol. 415, no. 6870, pp. 436-442, 2002.
[27]
U. Alon, N. Barkai, D. A. Notterman, K. Gish, S. Ybarra, D. Mack, and A. J. Levine, "Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays," in Proc. Nat. Acad. Sci., 1999, vol. 96, no. 12, pp. 6745-6750.
[28]
A. A. Alizadeh, M. B. Eisen, R. E. Davis, C. Ma, I. S. Lossos, A. Rosenwald, J. C. Boldrick, H. Sabet, T. Tran, X. Yu, J. I. Powell, L. Yang, G. E. Marti, T. Moore, J. J. Hudson, L. Lu, D. B. Lewis, R. Tibshirani, G. Sherlock, W. C. Chan, T. C. Greiner, D. D. Weisenburger, J. O. Armitage, R. Warnke, R. Levy, W. Wilson, M. R. Grever, J. C. Byrd, D. Botstein, P. O. Brown, and L. M. Staudt, "Distinct types of diffuse large b-cell lymphoma identified by gene expression profiling," Nature, vol. 403, no. 6769, pp. 503-511, 2000.
[29]
J. Khan, J. S. Wei, M. Ringner, L. H. Saal, M. Ladanyi, F. Westermann, F. Berthold, M. Schwab, C. R. Antonescu, C. Peterson, 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.
[30]
J. D. Spurrier, "On the null distribution of the kruskal-wallis statistic," Nonparametric Statist., vol. 15, no. 6, pp. 685-691, 2003.
[31]
F. R. Chung, Spectral Graph Theory. American Mathematical Soc., Washington, DC, vol. 92, 1997.
[32]
A. P. Bradley, "The use of the area under the ROC curve in the evaluation of machine learning algorithms," Pattern Recog., vol. 30, no. 7, pp. 1145-1159, 1997.
[33]
Z.-H. Zhou and X.-Y. Liu, "Training cost-sensitive neural networks with methods addressing the class imbalance problem," IEEE Trans. Knowl. Data Eng., vol. 18, no. 1, pp. 63-77, Jan. 2006.
[34]
Y. Piao, M. Piao, K. Park, and K. H. Ryu, "An ensemble correlation-based gene selection algorithm for cancer classification with gene expression data," Bioinformatics, vol. 28, no. 24, pp. 3306- 3315, 2012.
[35]
C.-C. Chang and C.-J. Lin, "Libsvm: A library for support vector machines," ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, p. 27, 2011.
[36]
S.-L. Wang, Y.-H. Zhu, W. Jia, and D.-S. Huang, "Robust classification method of tumor subtype by using correlation filters," IEEE/ACM Trans. Comput. Biol. Bioinf., vol. 9, no. 2, pp. 580-591, Mar.-Apr. 2012.
[37]
X. He, D. Cai, Y. Shao, H. Bao, and J. Han, "Laplacian regularized Gaussian mixture model for data clustering," IEEE Trans. Knowl. Data Eng., vol. 23, no. 9, pp. 1406-1418, Sep. 2011.
[38]
Q. Gu, Z. Li, and J. Han, "Joint feature selection and subspace learning," in Proc.-Int. Joint Conf. Artif. Intell., 2011, vol. 22, no. 1, p. 1294.
[39]
D. W. Huang, B. T. Sherman, and R. A. Lempicki, "Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists," Nucleic Acids Res., vol. 37, no. 1, pp. 1-13, 2009.
[40]
D. Husmeier, "Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks," Bioinformatics, vol. 19, no. 17, pp. 2271-2282, 2003.
[41]
M. Kapushesky, P. Kemmeren, A. C. Culhane, S. Durinck, J. Ihmels, C. Körner, M. Kull, A. Torrente, U. Sarkans, J. Vilo, and A. Brazma, "Expression profiler: Next generationan online platform for analysis of microarray data," Nucleic Acids Res., vol. 32, no. suppl 2, pp. W465-W470, 2004.
Recommendations
Identification and analysis of the regulatory network of Myc and microRNAs from high-throughput experimental data
As a transcription factor, c-Myc exerts significant influence in cancer development by regulating transcription of a large number of target genes including microRNAs. However, details of regulatory networks composed of Myc, microRNAs, and microRNA ...
Feature cluster selection for high-throughput data analysis
Feature selection is effective in selecting predictive gene sets for microarray classification. However, the large number of predictive gene sets and the disparity among them presents a challenge for identifying potential biomarkers. To facilitate ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Copyright © IEEE.
Publisher
IEEE Computer Society Press
Washington, DC, United States
Publication History
Published: 01 November 2015
Published in TCBB Volume 12, Issue 6
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 30Total Downloads
- Downloads (Last 12 months)1
- Downloads (Last 6 weeks)0
Reflects downloads up to 16 Nov 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in