Abstract
In recent years, microarrays have been shown to be an effective method for studying various biological processes, e.g., to improve our understanding of diseases such as cancer. In a typical situation, microarrays can be seen as large matrices in which rows and columns represent expression values of thousands of genes and tens of conditions such as samples from various patients. Several statistical techniques have been proposed in the literature to analyze the gene expression matrices. Towards that end, biclustering has been demonstrated to be one of the most effective methods for discovering gene expression patterns under various conditions. In this paper, we present a methodology to take advantage of the homogeneously expressed genes in biclusters to construct a classifier for sample class membership prediction. Our extensive experiments on 8 real cancer microarray datasets (4 diagnostic and 4 prognostic) show that our proposed classifier performed superior in both cancer diagnosis and prognosis, the latter of which was regarded quite difficult previously. Additionally, our results demonstrate that sample classification accuracy can serve as a good subjective quality measure for different types of biclusters, and hence as a tool to extrinsically evaluate the performance of various biclustering algorithms that produce those biclusters.
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Acknowledgements
The authors would like to thank Guohui Lin for his guidance on this work and Zhipeng Cai for providing the author with a number of microarray datasets used in this study. This research was supported by the Economic Development Board and the National Research Foundation of Singapore.
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Malhotra, B., Dahlmeier, D., Nandan, N. (2014). A Biclustering-Based Classification Framework for Microarray Analysis. In: Peng, WC., et al. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8643. Springer, Cham. https://doi.org/10.1007/978-3-319-13186-3_39
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DOI: https://doi.org/10.1007/978-3-319-13186-3_39
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