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The grand challenge of Alzheimer's disease (AD) are the early detection, accurately diagnoising, and the the reconstruction of genes signal pathways and its regulatory network. In our study, we use informatics methods to extract significant genes and reconstruct AD gene regulatory network. To avoid the limitation of traditional clustering methods which group genes in only one class and base on the...
The wide use of DNA microarray technology provide an increasingly detailed view of human transcriptome from hundreds to thousands of genes. The main challenge now is to extract valuable biological information from the colossal amount of data to extract information about pathways and regulatory network underlying the biological processes. In our study, independent component analysis (ICA) is applied...
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...
Rapid progress in deciphering the biological mechanism of Alzheimer's disease (AD) has arisen from the application of molecular and cell biology to this complex disorder of the limbic and association cortices. The precise diagnosis of AD, however, has little progress and is also a challenging task. In this study, we investigate the DNA gene expression data of AD based on independent component analysis...
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