Abstract
Continuous data mining has led to the generation of multi class datasets through microarray technology. New improved algorithms are then required to process and interpret these data. Cancer prediction tailored with variable selection process has shown to improve the overall prediction accuracy. Through variable selection process, the amount of informative genes gathered are much lesser than the initial data, yet the selective subset present in other methods cannot be fine-tuned to suit the necessity for particular number of variables. Hence, an improved technique of various variable range selection based on Random Forest method is proposed to allow selective variable subsets for cancer prediction. Our results indicate improvement in the overall prediction accuracy of cancer data based on the improved various variable range selection technique which allows selective variable selection to create best subset of genes. Moreover, this technique can assist in variable interaction analysis, gene network analysis, gene-ranking analysis and many other related fields.
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References
Paz, J.L., Seeberger, P.H.: Recent Advances and Future Challenges in Glycan Microarray Technology Carbohydrate Microarrays. In: Chevolot, Y. (ed.), vol. 808, pp. 1–12. Humana Press (2012)
Liew, A.W.-C., Law, N.-F., Yan, H.: Missing value imputation for gene expression data: computational techniques to recover missing data from available information. Briefings in Bioinformatics 12, 498–513 (2011)
Duval, B., Hao, J.-K.: Advances in metaheuristics for gene selection and classification of microarray data. Briefings in Bioinformatics 11, 127–141 (2010)
Wu, D., Rice, C., Wang, X.: Cancer bioinformatics: A new approach to systems clinical medicine. BMC Bioinformatics 13, 71 (2012)
Van Steen, K.: Travelling the world of gene–gene interactions. Briefings in Bioinformatics 13, 1–19 (2012)
Hua, J., Tembe, W.D., Dougherty, E.R.: Performance of feature-selection methods in the classification of high-dimension data. Pattern Recogn. 42, 409–424 (2009)
Wong, G., Leckie, C., Kowalczyk, A.: FSR: feature set reduction for scalable and accurate multi-class cancer subtype classification based on copy number. Bioinformatics 28, 151–159 (2012)
Nanni, L., Brahnam, S., Lumini, A.: Combining multiple approaches for gene microarray classification. Bioinformatics 28, 1151–1157 (2012)
Lin, W.-J., Chen, J.J.: Class-imbalanced classifiers for high-dimensional data. Briefings in Bioinformatics (2012)
Boulesteix, A.-L., Bender, A., Lorenzo Bermejo, J., Strobl, C.: Random forest Gini importance favours SNPs with large minor allele frequency: impact, sources and recommendations. Briefings in Bioinformatics 304, 292–304 (2012)
Breiman, L.: Random Forests. Mach. Learn. 45, 5–32 (2001)
Diaz-Uriarte, R., Alvarez de Andres, S.: Gene selection and classification of microarray data using random forest. BMC Bioinformatics 7, 3 (2006)
Moorthy, K., Mohamad, M.S.: Random forest for gene selection and microarray data classification. Bioinformation 7, 142–146 (2011)
Koukouvinos, C., Parpoula, C.: Variable Selection and Computation of the Prior Probability of a Model via ROC Curves Methodology. Journal of Data Science 10, 653–672 (2012)
Wang, H., Lo, S.-H., Zheng, T., Hu, I.: Interaction-based feature selection and classification for high-dimensional biological data. Bioinformatics 28, 2834–2842 (2012)
van ’t Veer, L.J., Dai, H., van de Vijver, M.J., He, Y.D., Hart, A.A., Mao, M., Peterse, H.L., van der Kooy, K., Marton, M.J., Witteveen, A.T., Schreiber, G.J., Kerkhoven, R.M., Roberts, C., Linsley, P.S., Bernards, R., Friend, S.H.: Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530–536 (2002)
Alizadeh, A.A., Eisen, M.B., Davis, R.E., Ma, C., Lossos, I.S., Rosenwald, A., Boldrick, J.C., Sabet, H., Tran, T., Yu, X., Powell, J.I., Yang, L., Marti, G.E., Moore, T., Hudson, J., Lu, L., Lewis, D.B., Tibshirani, R., Sherlock, G., Chan, W.C., Greiner, T.C., Weisenburger, D.D., Armitage, J.O., Warnke, R., Levy, R., Wilson, W., Grever, M.R., Byrd, J.C., Botstein, D., Brown, P.O., Staudt, L.M.: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000)
Khan, J., Wei, J.S., Ringner, M., Saal, L.H., Ladanyi, M., Westermann, F., Berthold, F., Schwab, M., Antonescu, C.R., Peterson, C., Meltzer, P.S.: Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat. Med. 7, 673–679 (2001)
Pomeroy, S.L., Tamayo, P., Gaasenbeek, M., Sturla, L.M., Angelo, M., McLaughlin, M.E., Kim, J.Y., Goumnerova, L.C., Black, P.M., Lau, C., Allen, J.C., Zagzag, D., Olson, J.M., Curran, T., Wetmore, C., Biegel, J.A., Poggio, T., Mukherjee, S., Rifkin, R., Califano, A., Stolovitzky, G., Louis, D.N., Mesirov, J.P., Lander, E.S., Golub, T.R.: Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature 415, 436–442 (2002)
Ross, D.T., Scherf, U., Eisen, M.B., Perou, C.M., Rees, C., Spellman, P., Iyer, V., Jeffrey, S.S., Van de Rijn, M., Waltham, M., Pergamenschikov, A., Lee, J.C., Lashkari, D., Shalon, D., Myers, T.G., Weinstein, J.N., Botstein, D., Brown, P.O.: Systematic variation in gene expression patterns in human cancer cell lines. Nat. Genet. 24, 227–235 (2000)
Efron, B., Tibshirani, R.: Improvements on Cross-Validation: The.632+ Bootstrap Method. Journal of the American Statistical Association 92, 548–560 (1997)
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Moorthy, K., Mohamad, M.S., Deris, S. (2013). Multiclass Prediction for Cancer Microarray Data Using Various Variables Range Selection Based on Random Forest. In: Li, J., et al. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7867. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40319-4_22
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DOI: https://doi.org/10.1007/978-3-642-40319-4_22
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