Abstract
One challenge in microarray analysis is to discover and capture valuable knowledge to understand biological processes and human disease mechanisms. Nonnegative Matrix Factorization (NMF) – a constrained optimization mechanism which decomposes a data matrix in terms of additive combination of non-negative factors– has been demonstrated to be a useful tool to reduce the dimension of gene expression data and to identify potentially interesting genes which explain latent structure hidden in microarray data.
In this paper, we detail how to use Nonnegative Matrix Factorization based on generalized Kullback-Leibler divergence to analyze gene expression profile data related to the cell line of mammary cancer MCF-7 and to pharmaceutical compounds connected to the metabolism of arachidonic acid. NMF technique is able to reduce the dimension of the considered genes-compounds matrix from thousands of genes to few metagenes and to extract information about the drugs that more affect these genes. We provide an experimental framework illustrating the technical steps one has to perform to use NMF to discover useful patterns from microarray data. In fact, the results obtained by NMF method could be used to select and characterize therapies that can be effective on biological functions involved in the neoplastic transformation process and to perform further biological investigations.
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Del Buono, N., Esposito, F., Fumarola, F., Boccarelli, A., Coluccia, M. (2016). Breast Cancer’s Microarray Data: Pattern Discovery Using Nonnegative Matrix Factorizations. In: Pardalos, P., Conca, P., Giuffrida, G., Nicosia, G. (eds) Machine Learning, Optimization, and Big Data. MOD 2016. Lecture Notes in Computer Science(), vol 10122. Springer, Cham. https://doi.org/10.1007/978-3-319-51469-7_24
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DOI: https://doi.org/10.1007/978-3-319-51469-7_24
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