Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30850
Automatic Threshold Search for Heat Map Based Feature Selection: A Cancer Dataset Analysis

Authors: Carlos Huertas, Reyes Juarez-Ramirez


Public health is one of the most critical issues today; therefore, there is great interest to improve technologies in the area of diseases detection. With machine learning and feature selection, it has been possible to aid the diagnosis of several diseases such as cancer. In this work, we present an extension to the Heat Map Based Feature Selection algorithm, this modification allows automatic threshold parameter selection that helps to improve the generalization performance of high dimensional data such as mass spectrometry. We have performed a comparison analysis using multiple cancer datasets and compare against the well known Recursive Feature Elimination algorithm and our original proposal, the results show improved classification performance that is very competitive against current techniques.

Keywords: Cancer, Mass Spectrometry, Feature selection, biomarker discovery

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1118


[1] J. Deng, A. Berg, and L. Fei-Fei, “Hierarchical semantic indexing for large scale image retrieval,” in Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, June 2011, pp. 785–792.
[2] S.-Y. Kung and M.-W. Mak, Feature Selection for Genomic and Proteomic Data Mining. John Wiley & Sons, Inc., 2008, pp. 1–45. (Online). Available:
[3] R. ”Bellman, ”Dynamic Programming”, ”1” ed. ”Princeton, NJ, USA”: ”Princeton University Press”, ”1957”.
[4] A. Y. Ng, “On feature selection: Learning with exponentially many irrelevant features as training examples,” in Proceedings of the Fifteenth International Conference on Machine Learning. Morgan Kaufmann, 1998, pp. 404–412.
[5] I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,” J. Mach. Learn. Res., vol. 3, pp. 1157–1182, 2003. (Online). Available:
[6] J. Yu and X.-W. Chen, “Bayesian neural network approaches to ovarian cancer identification from high-resolution mass spectrometry data,” Bioinformatics, vol. 21, no. 1, pp. 487–494, Jan. 2005. (Online). Available:
[7] S. Datta and L. M. DePadilla, “Feature selection and machine learning with mass spectrometry data for distinguishing cancer and non-cancer samples,” Statistical Methodology, vol. 3, no. 1, pp. 79 – 92, 2006, bioinformatics. (Online). Available:
[8] P. R. Srinivas, M. Verma, Y. Zhao, and S. Srivastava, “Proteomics for cancer biomarker discovery,” Clinical Chemistry, vol. 48, no. 8, pp. 1160–1169, 2002. (Online). Available:
[9] M. D. I. C. W. E. C. L. H. S. O. T. E. R. Kuschner, Karl W., “A bayesian network approach to feature selection in mass spectrometry data,” BMC Bioinformatics, vol. 11, 2010. (Online). Available:
[10] D. I. Malyarenko, W. E. Cooke, B.-L. Adam, G. Malik, H. Chen, E. R. Tracy, M. W. Trosset, M. Sasinowski, O. J. Semmes, and D. M. Manos, “Enhancement of sensitivity and resolution of surface-enhanced laser desorption/ionization time-of-flight mass spectrometric records for serum peptides using time-series analysis techniques,” Clinical Chemistry, vol. 51, no. 1, pp. 65–74, 2005. (Online). Available:
[11] I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, “Gene selection for cancer classification using support vector machines,” Mach. Learn., vol. 46, no. 1-3, pp. 389–422, 2002. (Online). Available:
[12] O. Chapelle, S. Keerthi, O. Chapelle, and S. Keerthi, “Multi-class feature selection with support vector machines,” in Proceedings of the American Statistical Association, 2008.
[13] B. Dittmann and S. Nitz, “Strategies for the development of reliable qa/qc methods when working with mass spectrometry-based chemosensory systems,” Sensors and Actuators B: Chemical, vol. 69, no. 3, pp. 253 – 257, 2000, proceedings of the International Symposium on Electronic Noses. (Online). Available:
[14] U. Depczynski, V. Frost, and K. Molt, “Genetic algorithms applied to the selection of factors in principal component regression,” Analytica Chimica Acta, vol. 420, no. 2, pp. 217 – 227, 2000. (Online). Available:
[15] M. Suganthy and P. Ramamoorthy, “Principal component analysis based feature extraction, morphological edge detection and localization for fast iris recognition.”
[16] M. Dash and H. Liu, “Feature selection for classification,” Intelligent Data Analysis, vol. 1, no. 14, pp. 131 – 156, 1997. (Online). Available:
[17] A. L. Blum and P. Langley, “Selection of relevant features and examples in machine learning,” Artificial Intelligence, vol. 97, no. 12, pp. 245 – 271, 1997. (Online). Available:
[18] S. Das, “Filters, wrappers and a boosting-based hybrid for feature selection,” in Proceedings of the Eighteenth International Conference on Machine Learning, ser. ICML ’01. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2001, pp. 74–81. (Online). Available:
[19] C. Huertas and R. Ju´arez-Ram´ırez, “Heat map based feature selection: A case study for ovarian cancer,” in Applications of Evolutionary Computation - 18th European Conference, EvoApplications 2015, Copenhagen, Denmark, April 8-10, 2015, Proceedings, 2015, pp. 3–13.
[20] H. Liu and R. Setiono, “Chi2: Feature selection and discretization of numeric attributes,” in In Proceedings of the Seventh International Conference on Tools with Artificial Intelligence, 1995, pp. 388–391.
[21] L. Yu and H. Liu, “Feature selection for high-dimensional data: A fast correlation-based filter solution,” 2003, pp. 856–863.
[22] K. Kira and L. A. Rendell, “A practical approach to feature selection,” in Proceedings of the Ninth International Workshop on Machine Learning, ser. ML92. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1992, pp. 249–256. (Online). Available:
[23] Y. Liu, “Feature extraction and dimensionality reduction for mass spectrometry data,” Comput. Biol. Med., vol. 39, no. 9, pp. 818–823, Sep 2009.
[24] T. Abeel, T. Helleputte, Y. Van de Peer, P. Dupont, and Y. Saeys, “Robust biomarker identification for cancer diagnosis with ensemble feature selection methods,” Bioinformatics, vol. 26, no. 3, pp. 392–398, Feb. 2010. (Online). Available:
[25] H. Kim, J. Watkinson, and D. Anastassiou, “Biomarker discovery using statistically significant gene sets.” Journal of Computational Biology, vol. 18, no. 10, pp. 1329–1338, 2011.
[26] F. Gonzlez and L. A. B. Muoz, “Feature selection for microarray gene expression data using simulated annealing guided by the multivariate joint entropy,” CoRR, vol. abs/1302.1733, 2013.
[27] L. Yang, S. Lv, and J. Wang, “Model-free variable selection in reproducing kernel hilbert space,” Journal of Machine Learning Research, vol. 17, no. 82, pp. 1–24, 2016. (Online). Available:
[28] L. Yu and H. Liu, “Efficient feature selection via analysis of relevance and redundancy,” J. Mach. Learn. Res., vol. 5, pp. 1205–1224, dec 2004. (Online). Available:
[29] G. H. John, R. Kohavi, and K. Pfleger, “Irrelevant features and the subset selection problem,” in MACHINE LEARNING: PROCEEDINGS OF THE ELEVENTH INTERNATIONAL. Morgan Kaufmann, 1994, pp. 121–129.
[30] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
[31] U. Alon, N. Barkai, D. Notterman, K. Gish, S. Ybarra, D. Mack, and A. Levine, “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, vol. 96, no. 12, pp. 6745–6750, jun 1999.
[32] D. Chowdary, J. Lathrop, J. Skelton, K. Curtin, T. Briggs, Y. Zhang, J. Yu, Y. Wang, and A. Mazumder, “Prognostic gene expression signatures can be measured in tissues collected in rnalater preservative,” The Journal of Molecular Diagnostics, vol. 8, no. 1, pp. 31–39, feb 2006.
[33] Gravier, Eleonore, G. Pierron, A. Vincent-Salomon, N. gruel, V. Raynal, A. Savignoni, Y. De Rycke, J.-Y. Pierga, C. Lucchesi, F. Reyal, A. Fourquet, S. Roman-Roman, F. Radvanyi, X. Sastre-Garau, B. Asselain, and O. Delattre, “A prognostic DNA signature for T1T2 node-negative breast cancer patients.” Genes, Chromosomes and Cancer, vol. 49, no. 12, pp. 1125–1125, Sep. 2010.
[34] E. Tian, F. Zhan, R. Walker, E. Rasmussen, Y. Ma, B. Barlogie, and J. D. Shaughnessy, Jr., “The role of the wnt-signaling antagonist dkk1 in the development of osteolytic lesions in multiple myeloma,” New England Journal of Medicine, vol. 349, no. 26, pp. 2483–2494, dec 2003.
[35] K. Crammer, O. Dekel, J. Keshet, S. Shalev-Shwartz, and Y. Singer, “Online passive-aggressive algorithms,” J. Mach. Learn. Res., vol. 7, pp. 551–585, dec 2006. (Online). Available:
[36] L. Breiman, “Random forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, oct 2001. (Online). Available:
[37] R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin, “Liblinear: A library for large linear classification,” J. Mach. Learn. Res., vol. 9, pp. 1871–1874, jun 2008. (Online). Available: