Discovering differences in gender-related skeletal muscle aging through the majority voting-based identification of differently expressed genes

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Dreder, Abdouladeem ; Tahir, Muhammad ; Seker, Huseyin ; Anwar, Naveed (2016)

Understanding gene function (GF) is still a significant challenge in system biology. Previously, several machine learning and computational techniques have been used to understand GF. However, these previous attempts have not produced a comprehensive interpretation of the relationship between genes and differences in both age and gender. Although there are several thousands of genes, very few differentially expressed genes play an active role in understanding the age and gender differences. The core aim of this study is to uncover new biomarkers that can contribute towards distinguishing between male and female according to the gene expression levels of skeletal muscle (SM) tissues. In our proposed multi-filter system (MFS), genes are first sorted using three different ranking techniques (t-test, Wilcoxon and Receiver Operating Characteristic (ROC)). Later, important genes are acquired using majority voting based on the principle that combining multiple models can improve the generalization of the system. Experiments were conducted on Micro Array gene expression dataset and results have indicated a significant increase in classification accuracy when compared with existing system.
  • References (21)
    21 references, page 1 of 3

    [1] S. Welle, R. Tawil, and C. A. Thornton, “Sex-related differences in gene expression in human skeletal muscle”, PLoS One, vol. 3, no. 1, pp. e1385-e1385, 2008

    [2] D. Liu, M. A. sartor, G. A. nader, E. E. pistilli, L. tanton, C. Lilly, et al., “Microarray analysis reveals novel features of the muscle aging process in men and women”, Biological Sciences, vol. 68, no.9, pp. 1035-1044, 2013

    [3] D. D. Liu, M. A. Sartor, G. A. Nader, L. Gutmann, M. K. Treutelaar, E. E. Pistilli, H. B. IglayReger, C. F. Burant, E. P. Hoffman, and P. M. Gordon, “Skeletal muscle gene expression in response to resistance exercise: sex specific regulation”, BMC genomics, vol. 11, no. 1, pp. 659, 2010.

    [4] G. Sifakis, I. Valavanis, O. Papadodima, and A. A. Chatziioannou, “Identifying Gender Independent Biomarkers Responsible for human Muscle Aging Using Microarray Data”, Bioinformatics and Bioengineering (BIBE), no. pp. 1-5, 2013

    [5] S. M. Roth, R. E. Ferrell, D. G. Peters, E. J. Metter, B. F. Hurley, and M. A. Rogers, “Influence of age, sex, and strength training on human muscle gene expression determined by microarray”, Physiological genomics, vol. 10, pp. 181-190, 2002.

    [6] Y. Saeys, I. a. Inza, and P. Larranaga, ”A review of feature selection techniques in bioinformatics”, bioinformatics, vol. 23, no. pp. 2507-2517, 2007.

    [7] Y. Su, T. M. Murali, V. Pavlovic, M. Schaffer, and S. Kasif, “RankGene: identification of diagnostic genes based on expression data”, BIOINFORMATICS, vol. 19, pp. 1578-1579, 2003

    [8] K. Murphy. “Machine learning: a probabilistic perspective”. Cambridge MA: MIT Press, 2012.

    [9] N. Thouleimat, D. Hernandez-Lobato, and P. Dupont, “Variance Estimators for t-Test Ranking Influence the Stability and Predictive Performance of Microarray Gene Signatures”, European Conference on Computational Biology, 2010.

    [10] S. Sahan, K. Polata, H. Kodazb, and S. Gne, “Anewhybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis”, ELSEVIER, vol. 37, no. pp. 415-423, 2007.

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