Abstract
Accurate diagnosis of neuroblastoma, non-Hodgkin lymphoma, rhabdomyosarcoma, and Ewing sarcoma, is often difficult because these cancers appear similar in routine histology. Finding a few useful biomarkers (not all related genes) that can discriminate between the subgroups will help designing better diagnostic systems. In an earlier study we reported a set of seven genes having excellent discrimination power. In this investigation we extend that study and find other distinct sets of genes with strong class specific signatures. This is achieved analyzing the correlation between genes. This led us to find another set of seven genes with better discriminating power. Our original gene selection method used a neural network whose output may significantly depend on initialization of the network, network size as well as the training data set. To address these issues we propose a scheme based on re-sampling. This method can also reduce the effect wide variation in number of data points in the training set from different classes. This method led us to find a set of five genes with good discriminating power. The genes identified by the proposed methods have roles in cancer biology.
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Efron, B., Tibshirani, R.J.: An Introduction to the Bootstrap. Chapman and Hall, Boca Raton (1993)
Fu, L.M., Fu-Liu, C.S.: Evaluation of Gene Importance in Microarray Data Based upon Probability of Selection. BMC Bioinformatics 6, 67 (2005)
Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D., Lander, E.S.: Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science 286, 531–537 (1999)
Hong, H., Tong, W., Perkins, R., Fang, H., Xie, Q., Shi, L.: Multiclass Decision Forest – A Novel Pattern Recognition Method for Multiclass Classification in Microarray Data Analysis. DNA and Cell Biology 23(10), 685–694 (2004)
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. Nature Medicine 7, 673–679 (2001)
Pal, N.R., Aguan, K., Sharma, A., Amari, S.I.: Discovering Biomarkers from Gene Expression Data for Predicting Cancer Subgroups Using Neural Networks and Relational Fuzzy Clustering. BMC Bioinformatics 8, 5 (2007)
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)
Tibshirani, R., Hastie, T., Narasimhan, B., Chu, G.: Diagnosis of Multiple Cancer Types by Shrunken Centroids of Gene Expression. Proc. Natl. Acad. Sci. 99(10), 6567–6572 (2002)
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Tsai, YS., Chung, IF., Lin, CT., Pal, N.R. (2008). Identification of Different Sets of Biomarkers for Diagnostic Classification of Cancers. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4985. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69162-4_90
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DOI: https://doi.org/10.1007/978-3-540-69162-4_90
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69159-4
Online ISBN: 978-3-540-69162-4
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