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Unsupervised machine learning approaches are efficient analysis tools for DNA microarray technique which can accumulate hundreds of thousands of genes expression levels in a single experiment. In our study, two unsupervised knowledge-based matrix factorization methods, independent component analysis (ICA) and nonnegative matrix factorization (NMF) are explored to identify significant genes and related...
Gene microarray technology is an effective tool to monitor simultaneous activity of multiple cellular pathways from thousands of genes in a single chip. Many clustering methods have been developed to identify groups of genes or experimental conditions that exhibit similar expression patterns from gene expression data, such as hierarchical clustering, k-means, and self-organizing maps (SOM). The limitations...
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