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Optimization Based Tumor Classification from Microarray Gene Expression Data

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  • Onur Dagliyan
  • Fadime Uney-Yuksektepe
  • I Halil Kavakli
  • Metin Turkay

Abstract

Background: An important use of data obtained from microarray measurements is the classification of tumor types with respect to genes that are either up or down regulated in specific cancer types. A number of algorithms have been proposed to obtain such classifications. These algorithms usually require parameter optimization to obtain accurate results depending on the type of data. Additionally, it is highly critical to find an optimal set of markers among those up or down regulated genes that can be clinically utilized to build assays for the diagnosis or to follow progression of specific cancer types. In this paper, we employ a mixed integer programming based classification algorithm named hyper-box enclosure method (HBE) for the classification of some cancer types with a minimal set of predictor genes. This optimization based method which is a user friendly and efficient classifier may allow the clinicians to diagnose and follow progression of certain cancer types. Methodology/Principal Findings: We apply HBE algorithm to some well known data sets such as leukemia, prostate cancer, diffuse large B-cell lymphoma (DLBCL), small round blue cell tumors (SRBCT) to find some predictor genes that can be utilized for diagnosis and prognosis in a robust manner with a high accuracy. Our approach does not require any modification or parameter optimization for each data set. Additionally, information gain attribute evaluator, relief attribute evaluator and correlation-based feature selection methods are employed for the gene selection. The results are compared with those from other studies and biological roles of selected genes in corresponding cancer type are described. Conclusions/Significance: The performance of our algorithm overall was better than the other algorithms reported in the literature and classifiers found in WEKA data-mining package. Since it does not require a parameter optimization and it performs consistently very high prediction rate on different type of data sets, HBE method is an effective and consistent tool for cancer type prediction with a small number of gene markers.

Suggested Citation

  • Onur Dagliyan & Fadime Uney-Yuksektepe & I Halil Kavakli & Metin Turkay, 2011. "Optimization Based Tumor Classification from Microarray Gene Expression Data," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-10, February.
    • Handle: RePEc:plo:pone00:0014579
    DOI: 10.1371/journal.pone.0014579
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    References listed on IDEAS

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    1. Naijun Sha & Marina Vannucci & Mahlet G. Tadesse & Philip J. Brown & Ilaria Dragoni & Nick Davies & Tracy C. Roberts & Andrea Contestabile & Mike Salmon & Chris Buckley & Francesco Falciani, 2004. "Bayesian Variable Selection in Multinomial Probit Models to Identify Molecular Signatures of Disease Stage," Biometrics, The International Biometric Society, vol. 60(3), pages 812-819, September.
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    Cited by:

    1. Zhenqiu Liu & Dechang Chen & Li Sheng & Amy Y Liu, 2013. "Class Prediction and Feature Selection with Linear Optimization for Metagenomic Count Data," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-7, March.
    2. Bertolazzi, P. & Felici, G. & Festa, P. & Fiscon, G. & Weitschek, E., 2016. "Integer programming models for feature selection: New extensions and a randomized solution algorithm," European Journal of Operational Research, Elsevier, vol. 250(2), pages 389-399.
    3. Fadime Üney-Yüksektepe, 2014. "A novel approach to cutting decision trees," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 22(3), pages 553-565, September.

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