Abstract
Recently, some non-coding small RNAs, known as microRNAs (miRNA), have drawn a lot of attention to identify their role in gene regulation and various biological processes. The miRNA profiles are surprisingly informative, reflecting the malignancy state of the tissues. In this paper, we attempt to explore extensive features and classifiers through a comparative study of the most promising feature selection methods and machine learning classifiers. Here we use the expression profile of 217 miRNAs from 186 samples, including multiple human cancers. Pearson’s and Spearman’s correlation coefficients, Euclidean distance, cosine coefficient, information gain, mutual information and signal to noise ratio have been used for feature selection. Backpropagation neural network, support vector machine, and k-nearest neighbor have been used for classification. Experimental results indicate that k-nearest neighbor with cosine coefficient produces the best result, 95.0% of recognition rate on the test data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Golub, T., Slonim, D., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J., et al.: Moecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)
Cho, S.B., Won, H.H.: Machine learning in DNA microarray analysis for cancer classification. In: The First Asia Pacific Bioinformatics Conference (2003)
Segal, E., Shapira, M., Regev, A., Pe’er, D., Botstein, D., Koller, D., et al.: Module networks: Identifying regulatory modules and their condition-specific regulators from gene expression data. Nature Genetics 34, 166–176 (2003)
Stanford Microarray Database, http://smd.stanford.edu/
Gene Expression Omnibus, http://www.ncbi.nlm.nih.gov/geo/
Ambros, V.: The functions of animal microRNAs. Nature 431, 350–355 (2004)
Bartel, D.: MicroRNAs: Genomics, biogenesis, mechanism, and function. Cell 116, 281–297 (2004)
Lu, J., Getz, G., Miska, E.A., Alvarez-Saavedra, E., Lamb, J., Peck, D., Sweet-Cordero, A., et al.: MicroRNA expression profiles classify human cancers. Nature 435, 834–838 (2005)
Xu, R., Xu, J., Wunsch II, D.C.: MicroRNA expression profile based cancer classification using Default ARTMAP. Neural Networks 22, 774–780 (2009)
Zheng, Y., Kwoh, C.K.: Informative MicroRNA expression patterns for cancer classification. In: Li, J., Yang, Q., Tan, A.-H. (eds.) BioDM 2006. LNCS (LNBI), vol. 3916, pp. 143–154. Springer, Heidelberg (2006)
Cho, S.B.: Exploring features and classifiers to classify gene expression profiles of acute leukemia. International Journal of Pattern Recognition and Artificial Intelligence 16(7), 831–844 (2002)
Saeys, Y., Inza, I., Larranaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23, 2507–2517 (2007)
Su, Y., Murali, T.M., Pavlovic, V., Schaffer, M., Kasif, S.: RankGene: Identification of diagnostic genes based on expression data. Bioinformatics 19, 1578–1579 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kim, KJ., Cho, SB. (2010). Exploring Features and Classifiers to Classify MicroRNA Expression Profiles of Human Cancer. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-17534-3_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17533-6
Online ISBN: 978-3-642-17534-3
eBook Packages: Computer ScienceComputer Science (R0)