Feature Selection for Genomic Signal Processing: Unsupervised, Supervised, and Self-Supervised Scenarios | Journal of Signal Processing Systems Skip to main content

Advertisement

Springer Nature Link
Log in

Feature Selection for Genomic Signal Processing: Unsupervised, Supervised, and Self-Supervised Scenarios

  • Published:
Journal of Signal Processing Systems Aims and scope Submit manuscript

Abstract

An effective data mining system lies in the representation of pattern vectors. For many bioinformatic applications, data are represented as vectors of extremely high dimension. This motivates the research on feature selection. In the literature, there are plenty of reports on feature selection methods. In terms of training data types, they are divided into the unsupervised and supervised categories. In terms of selection methods, they fall into filter and wrapper categories. This paper will provide a brief overview on the state-of-the-arts feature selection methods on all these categories. Sample applications of these methods for genomic signal processing will be highlighted. This paper also describes a notion of self-supervision. A special method called vector index adaptive SVM (VIA-SVM) is described for selecting features under the self-supervision scenario. Furthermore, the paper makes use of a more powerful symmetric doubly supervised formulation, for which VIA-SVM is particularly useful. Based on several subcellular localization experiments, and microarray time course experiments, the VIA-SVM algorithm when combined with some filter-type metrics appears to deliver a substantial dimension reduction (one-order of magnitude) with only little degradation on accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11

Similar content being viewed by others

Notes

  1. For such a huge dimensionality, a preliminary Signal-to-Noise ratio (SNR)-based filtering method can be applied to weed out those k-mers patterns (i.e. columns) that are below certain low threshold.

  2. In addition to the SNR-type filter and SVM-RFE, there exist an extremely large number of application studies based on microarray data. Two recent ones are the MRMR [38] and Markov blanket [39], which are based on the Multivariate techniques. Another recent approach is the VIA-SVM [40], which is more amendable to the self-supervised scenario explained in Section 6.

  3. Downloadable from the official site http://www.genome.wi.mit.edu/mpr

  4. Here, we use bold face to represent both vectorial data such as gene expression profiles and non-vectorial data such as sequences.

References

  1. Guo, J., Mak, M. W., & Kung, S. Y. (2006). Eukaryotic protein subcellular localization based on local pairwise profile alignment SVM. In 2006 IEEE international workshop on machine learning for signal processing (MLSP’06) (pp. 391–396).

  2. Reinhardt, A., & Hubbard, T. (1998). Using neural networks for prediction of the subcellular location of proteins. Nucleic Acids Research, 26, 2230–2236.

    Article  Google Scholar 

  3. Pavlidis, P., Weston, J., Cai, J., & Grundy, W. N. (2001). Gene functional classification from heterogeneous data. In Int. conf. on computational biology (pp. 249–255). Pittsburgh: PA.

    Google Scholar 

  4. Leslie, C., ESKIN, E., & Noble, W. S. (2002). The spectrum kernel: A string kernel for SVM protein classification. In Altman, R. B., Dunker, A. K., Hunter, L., Lauredale, K., & Klein, T. E. (Eds.) Proc. of the pacific symposium on biocomputing. River Edge: World Scientific.

    Google Scholar 

  5. Leslie, C. S., Eskin, E., Cohen, A., Weston, J., & Noble, W. S. (2004). Mismatch string kernels for discriminative protein classification. Bioinformatics, 20(4), 467–476.

    Article  Google Scholar 

  6. Ben-Hur, A., & Brutlag, D. (2004). Sequence motifs: Highly predictive features of protein function. Neural Information Processing Systems 2004.

  7. Kuang, R., Ie, E., Wang, K., Wang, K., Siddiqi, M., Freund, Y., & Leslie, C. (2004). Profile-based string kernels for remote homology detection and motif extraction. Computational Systems Bioinformatics Conference, 2004. CSB 2004. Proceedings. 2004 IEEE (pp. 152–160).

  8. Gao, Q., & Wang, Z. (2006). Feature subset selection for protein subcellular localization prediction. Lecture Notes in Computer Science, (Vol. 4115, p. 433).

  9. Su, Y., Murali, T. M., Pavlovic, V., Schaffer, M., & Kasif, S. (2003). RankGene: Identification of diagnostic genes based on expression data (vol. 19). Oxford: Oxford University Press.

    Google Scholar 

  10. Kung, S. Y., & Mak, M. W. (2008). Feature selection for self-supervised classification with applications to microarray and sequence data. IEEE Journal of Selected Topics in Signal Processing: Special Issue on Genomic and Proteomic Signal Processing, 2, 297–309.

    Google Scholar 

  11. Huang, C., Lin, C., & Pal, N. (2003). Hierarchical learning architecture with automatic feature selection for multiclass protein fold classification. NanoBioscience, IEEE Transactions on, 2, 221–232.

    Article  Google Scholar 

  12. Kohavi, R., & John, G. H. (1997). Wrappers for feature selection. Artificial Intelligence, 97(1–2), 273–324.

    Article  MATH  Google Scholar 

  13. Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., et al. (1999). Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science, 286, 531–537.

    Article  Google Scholar 

  14. Kudo, M., & Sklansky, J. (2000). Comparison of algorithms that select features for pattern classifiers. Pattern Recognition, 33(1), 25–41.

    Article  Google Scholar 

  15. Simon, R. (2003). Diagnostic and prognostic prediction using gene expression profiles in high-dimensional microarray data. British Journal of Cancer, 89(9), 1599–1604.

    Article  Google Scholar 

  16. Hastie, T., Tibshirani, R., Eisen, M., Alizadeh, A., Levy, R., Staudt, L., et al. (2000). ’Gene shaving’ as a method for identifying distinct sets of genes with similar expression patterns. Genome Biology, 1(2), research0003.1–research0003.21.

    Article  Google Scholar 

  17. Ding, C. (2003). Unsupervised feature selection via two-way ordering in gene expression analysis. Bioinformatics, 19(10), 1259–1266.

    Article  Google Scholar 

  18. Varshavsky, R., Gottlieb, A., Linial, M., & Horn, D. (2006). Novel unsupervised feature filtering of biological data. Bioinformatics, 22(14), e507–e513.

    Article  Google Scholar 

  19. Golub, G. H., & Loan, C. F. V. (1996) Matrix computations. Baltimore: Johns Hopkins University Press.

    MATH  Google Scholar 

  20. Steinbach, M., Ertöz, L., & Kumar, V. (2003). The challenges of clustering high dimensional data. In: New vistas in statistical physics: Applications in econophysics, bioinformatics, and pattern recognition. New York: Springer.

    Google Scholar 

  21. Guyon, I., Elisseefi, A., & Kaelbling, L. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3(7–8), 1157–1182.

    Article  MATH  Google Scholar 

  22. Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., et al. (1999). Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. Proceedings of the National Academy of Sciences, 96, 2907–2912, Mar.

    Article  Google Scholar 

  23. Kohane, I. S., Kho, A. T., & Butte, A. J. (2003) Microarrays for an integrative genomics. Cambridge: MIT.

    Google Scholar 

  24. Xing, E., & Karp, R. (2001). CLIFF: Clustering of high-dimensional microarray data via iterative feature filtering using normalized cuts. Bioinformatics, 17(90001), 306–315.

    Google Scholar 

  25. Roth, V., & Lange, T. (2004). Bayesian class discovery in microarray datasets. Biomedical Engineering, IEEE Transactions on, 51(5), 707–718.

    Article  Google Scholar 

  26. Niijima, S., & Okuno, Y. (2008). Laplacian linear discriminant analysis approach to unsupervised feature selection. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 10, 20 Oct. doi:10.1109/TCBB.2007.70257.

  27. He, X., Cai, D., & Niyogi, P. (2005). Laplacian score for feature selection. Advances in Neural Information Processing Systems, 18, 507–514.

    Google Scholar 

  28. Wolf, L., & Shashua, A. (2005). Feature selection for unsupervised and supervised inference: The emergence of sparsity in a weight-based approach. The Journal of Machine Learning Research, 6, 1855–1887.

    MathSciNet  Google Scholar 

  29. Li, H., Jiang, T., & Zhang, K. (2006). Efficient and robust feature extraction by maximum margin criterion. Neural Networks, IEEE Transactions on, 17, 157–165.

    Article  Google Scholar 

  30. Fukunaga, K. (1990). Introduction to statistical pattern recognition. London: Academic.

    MATH  Google Scholar 

  31. Alon, U., Barkai, N., Notterman, D., Gish, K., Ybarra, S., Mack, D., et al. (1999). Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proceedings of the National Academy of Sciences of the United States of America, 96(12), 6745.

    Article  Google Scholar 

  32. Armstrong, S., Staunton, J., Silverman, L., Pieters, R., den Boer, M., Minden, M., et al. (2002). MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia. Nature Genetics, 30(1), 41–47.

    Article  Google Scholar 

  33. Fauquet, C., Desbois, D., Fargette, D., & Vidal, G. (1988). Classification of furoviruses based on the amino acid composition of their coat proteins. Viruses with fungal vectors (pp. 19–38). Wellesbourne: Association of Applied Biologists.

    Google Scholar 

  34. Pomeroy, S., Tamayo, P., Gaasenbeek, M., Sturla, L., Angelo, M., McLaughlin, M., et al. (2002). Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature, 415(6870), 436–442.

    Article  Google Scholar 

  35. van ’t Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A. M., Mao, M., et al. (2002). Gene expression profiling predicts clinical outcome of breast cancer. Nature, 415, 530–536.

    Article  Google Scholar 

  36. Beer, D. G., Kardia, S. L., Huang, C.-C., Giordano, T. J., Levin, A. M., Misek, D. E., et al. (2002). Gene-expression profiles predict survival of patients with lung adenocarcinoma. Natural Medicines, 8, 816–824.

    Google Scholar 

  37. Khan, J., Wei, J. S., Ringner, M., Saal, L. H., Ladanyi, M., Westermann, F., et al. (2001) Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Natural Medicines, 7, 673–679, June.

    Article  Google Scholar 

  38. Ding, C., & Peng, H. (2003). Minimum redundancy feature selection from microarray gene expression data. Bioinformatics Conference, 2003. CSB 2003. Proceedings of the 2003 IEEE (pp. 523–528).

  39. Gevaert, O., Smet, F. D., Timmerman, D., Moreau, Y., & Moor, B. D. (2006). Predicting the prognosis of breast cancer by integrating clinical and microarray data with bayesian networks. Bioinformatics, 22, 184–190.

    Article  Google Scholar 

  40. Kung, S. Y., & Mak, M. W. (2008). Machine learning for bioinformatics: An introduction to engineers. Cambridge: Cambridge University Press.

    Google Scholar 

  41. Mak, M. W., & Kung, S. Y. (2006). A solution to the curse of dimensionality problem in pairwise scoring techniques. In Int. conf. on neural information processing (pp. 314–323).

  42. Jafari, P., & Azuaje, F. (2006). An assessment of recently published gene expression data analyses: Reporting experimental design and statistical factors. BMC Medical Informatics, 6(27), 27–35.

    Article  Google Scholar 

  43. Baldi, P., & Brunak, S. (2001) Bioinformatics: The machine learning approach (2nd ed). Cambridge: MIT.

    MATH  Google Scholar 

  44. Fox, R. J., & Dimmic, M. W. (2006). A two-sample bayesian t-test for microarray data. BMC Bioinformatics, 7, 126.

    Article  Google Scholar 

  45. Dudoit, S., Fridlyand, J., & Speed, T. P. (2002). Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association, 97, 77–88.

    Article  MATH  MathSciNet  Google Scholar 

  46. Ben-Dor, A., Bruhn, L., Friedman, N., Nachman, I., Schummer, M., & Yakhini, Z. (2000). Tissue classification with gene expression profiles. Journal of Computational Biology, 7, 559–583.

    Article  Google Scholar 

  47. Mak, M. W., & Kung, S. Y. (2008). Fusion of feature selection methods for pairwise scoring svm. Neurocomputing, special issue for ICONIP’06.

  48. Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine Learning, 46, 389–422.

    Article  MATH  Google Scholar 

  49. Zhang, X. G., Lu, X., Shi, Q., Xu, X. Q., Leung, H. C. E., Harris, L. N., et al. (2006). Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data. BMC Bioinformatics, 7(197), 197–210.

    Article  Google Scholar 

  50. Golub, T. R., Slonim, D. K., Tamayo, C. H. P., Gaasenbeek, M., Mesirov, J. P., Coller, H., et al. (1999). Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science, 286, 531–537, Oct.

    Article  Google Scholar 

  51. Dudoit, S., Fridlyand, J., & Speed, T. P. (2000). Comparison of discrimination methods for the classification of tumors using gene expression data. Technical Report 576, Dept. of Statistics, University of California, Berkeley, Berkeley, CA 94720-3860.

  52. Smith, T. F., & Waterman, M. S. (1981). Comparison of biosequences. Advances in Applied Mathematics, 2, 482–489.

    Article  MATH  MathSciNet  Google Scholar 

  53. Huang, Y., & Li, Y. D. (2004). Prediction of protein subcellular locations using fuzzy K-NN method. Bioinformatics, 20(1), 21–28.

    Article  Google Scholar 

  54. Saeys, Y., Inza, I., & Larranaga, P. (2007). A review of feature selection techniques in bioinformatics. Bioinformatics, 23(19), 2507.

    Article  Google Scholar 

Download references

Acknowledgements

This work was in part supported by The Research Grant Council of the Hong Kong SAR (Project No. PolyU 5241/07E, PolyU 5251/08E, and A-PH18).

Author information

Authors and Affiliations

  1. Princeton University, Princeton, NJ, USA

    S. Y. Kung & Yuhui Luo

  2. National Chung Hsing University, 250 Kuo Kuang Rd., Taichung, 402, Taiwan, Republic of China

    S. Y. Kung

  3. Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong, SAR

    Man-Wai Mak

Authors
  1. S. Y. Kung
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Yuhui Luo
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Man-Wai Mak
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Yuhui Luo.

Additional information

Based on SY Kung’s Keynote Paper, Proceedings, IEEE Workshop on Machine Learning for Signal Processing, Thessaloniki, Greece, August 27–29, 2007.

The research was conducted in part while S.Y. Kung was on leave with the National Chung-Hsing University as a Chair Professor.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kung, S.Y., Luo, Y. & Mak, MW. Feature Selection for Genomic Signal Processing: Unsupervised, Supervised, and Self-Supervised Scenarios. J Sign Process Syst 61, 3–20 (2010). https://doi.org/10.1007/s11265-008-0273-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-008-0273-8

Keywords