Abstract
Identifying cancer biomarkers is an essential research problem that has attracted the attention of several research groups over the past decades. The main target is to find the most informative genes for predicting cancer cases, such genes are called cancer biomarkers. In this chapter, we contribute to the literature a new methodology that analysis the communities of genes to identify the most representative ones to be considered as biomarkers. The proposed methodology employs iterative t-test and singular value decomposition in order to produce the communities of genes which are analyzed further to identify the most prominent gene within each community; the latter genes are analyzed further as cancer biomarkers. The proposed methods have been applied on three microarray datasets. The reported results demonstrate the applicability and effectiveness of the proposed methodology.
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References
Jemal, A., Siegel, R., Ward, E., Xu, T., Thun, M.J.: Cancer statistics. A Cancer Journal for Clinicians 57, 43–66 (2007)
Butte, A.: The use and analysis of microarray data. Nature Reviews 1, 951–960 (2002)
Dembele, D., Kastner, P.: Fuzzy c-means method for clustering microarray data. Bioinformatics 19, 973–980 (2003)
Kohonen, T.: Self-organizing paps. Springer Series in Information Sciences, vol. 30. Springer, Heidelberg (2001)
Golub, T.R., Slonim, D., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J., Coller, H., Loh, M., Downing, J., Caligiuri, M., Bloomfield, C., Lender, E.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)
Hsu, C., Chang, C., Lin, C.: A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University (July 2003)
Toura, A., Basu, M.: Application of neural network to gene expression data for cancer classification. In: Proceedings of IEEE International Joint Conference on Neural Networks, pp. 583–587 (2001)
Alshalalfa, M., Alhajj, R.: Application of double clustering to gene expression data for class prediction. In: Proceedings of AINA Wokshops, vol. 1, pp. 733–736 (2007)
Alshalalf, M., Alhajj, R.: Attractive feature reduction approach for colon data classification. In: Proceedings of AINA Workshops, vol. 1, pp. 678–683 (2007)
Khabbaz, M., Kianmher, K., Alshalalfa, M., Alhajj, R.: Fuzzy classifier based feature reduction for better gene selection. In: Song, I.-Y., Eder, J., Nguyen, T.M. (eds.) DaWaK 2007. LNCS, vol. 4654, pp. 334–344. Springer, Heidelberg (2007)
Kianmehr, K., Alshalalfa, M., Alhajj, R.: Effectiveness of fuzzy discretization for class association rule-based classification. In: Proceedings of the International Symposium on Methodologies for Intelligent Systems. LNCS. Springer, Heidelberg (2008)
Varshavsky, R., Gottlieb, A., Linial, L., Horn, D.: Novel unsupervised feature filtering of biological data. Bioinformatics 22, 507–513 (2006)
Dudoit, S., Yang, Y.H., Callow, M., Speed, T.: Statistical methods for identifying differentiallyexpressed genes in replicated cdna microarray experiments. Technical Report #578, University of California, Berkeley (2000)
Alter, O., Brown, P., Botstein, D.: Singular value decomposition for genome-wide expression data processing and modeling. PNAS 97, 10101–10106 (2000)
Furey, T.S., Cristianini, N., Duffy, N., Bednarski, D.W., Schummer, M., Haussler, D.: Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16, 906–914 (2000)
Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Machine Learning 46, 389–422 (2002)
Li, J., Wong, L.: Identifying good diagnosis gene group from gene expression profile using the concept of emerging patterns. Bioinformatics 18, 725–734 (2002)
Zhang, X., Ke, H.: All/aml cancer classification by gene expression data using svm and csvm. Genomics informatics 11, 237–239 (2000)
Li, L., Pedersen, L.G., Darden, T.A., Weinberg, C.R.: Class prediction and discovery based on gene expression data. Iostatistics Branch and Lab of Structural Biology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina (2000)
Bijlani, R., Cheng, Y., Pearce, D., Brooks, A., Ogihara, M.: Prediction of biologically significant components from microarray data: independently consisitent expression discriminator(iced). Bioinformatics 19, 62–70 (2003)
Bicciato, S., Pandin, M., Didon, G., Bello, C.D.: Pattern identification and classification in gene expression data using an autoassociative neural network model. Biotechnology and Bioengineering 81, 594–606 (2002)
Moler, E., Chow, M., Mian, I.: Analysis of molecular profile data using generative and disciminative methods. Physiol. genomics 4, 109–126 (2000)
Wang, J., Hellem, T., Jonassen, I., Myklebost, O., Hovig, E.: Tumor classification and marker gene prediction by feature selection and fuzzy c-means clustering using microarray data. BMC Bioinformatics 4, 60–70 (2003)
Wang, L., Chu, F., Xie, W.: Accurate cancer calssification using expressions of very few genes. IEEE/ACM transactions on computational biology and bioinformatics 4, 40–53 (2007)
Chu, F., Wang, L.: Cancer classification with microarray data using support vector machines. Bioinformatics using computational intelligence paradigms 176, 167–189 (2005)
Chen, J., Tsai, C., Tzeng, S., Chen, C.: Gene selection with multiple ordering criteria. BMC Bioinformatics 8 (2007)
Jaeger, J., Sengupta, R., Ruzzo, W.L.: Improved gene selection for classification of microarrays. In: Proceedings of Pacific Symposium on Biocomputing, pp. 53–64 (2003)
Alon, U., Barkai, N., Notterman, D.A., Gish, K., Ybarra, S., Mack, D., Leving, A.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide array. PNAS 96, 6745–6750 (1999)
West, M., Dressman, H., Haung, E., Ishida, S., Spang, R., Zuzan, H., Olson, J., Marks, J., Nevins, J.R.: Predicting the clinical status of human breast cancer by using gene expression profiles. PNAS 98, 11562–11567 (2001)
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Alshalalfa, M., Qabaja, A., Alhajj, R., Rokne, J. (2009). Identifying Disease-Related Biomarkers by Studying Social Networks of Genes. In: Lim, C.P., Jain, L.C., Dehuri, S. (eds) Innovations in Swarm Intelligence. Studies in Computational Intelligence, vol 248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04225-6_12
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DOI: https://doi.org/10.1007/978-3-642-04225-6_12
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