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A linear mixture model of non-negative sources is used to dissect the gene expression data into components that are putative underlying active biological processes. Each biological process/component is characterized by its specific genes that are exclusively highly expressed in it and expected to be functional enriched; while a majority of all the genes maintain basic cellular structure and functions...
Gene module discovery can provide comprehensive molecular portrait of biological regulation and functional genomics. We present a new analytic strategy - nonnegative independent component analysis to reveal some gene module composite. The results show that by grouping genes in the latent space, we can find statistically more significant enrichment of gene annotations within clusters. Further, this...
For the critical task of gene module discovery in genomic research, we present a model-based hierarchical data clustering and visualization algorithm, VIsual Statistical Data Analyzer (VISDA), which effectively exploits human-data interaction to improve the clustering outcome. Guided by a diagnostic tree, we apply VISDA to a muscular dystrophy dataset that contains a number of different phenotypic...
For the critical task of gene module discovery in genomic research, we present a model-based hierarchical data clustering and visualization algorithm, visual statistical data analyzer (VISDA), which effectively exploits human-data interaction to improve the clustering outcome. Guided by a diagnostic tree, we apply VISDA to a muscular dystrophy dataset that contains a number of different phenotypic...
In this paper, we report a new gene clustering approach, non-negative independent component analysis (nICA), for microarray data analysis. Due to positive nature of molecular expressions, nICA fits better to the reality of corresponding putative biological processes. In conjunction with nICA model, visual statistical data analyzer (VISDA) is applied to group genes into modules in the latent variable...
For the critical task of gene module discovery in genomic research, we present a model-based hierarchical data clustering and visualization algorithm, visual statistical data analyzer (VISDA), which effectively exploits human-data interaction to improve the clustering outcome. Guided by a diagnostic tree, we apply VISDA to a muscular dystrophy dataset that contains a number of different phenotypic...
In this paper, we report a new gene clustering approach - non-negative independent component analysis (nICA) - for microarray data analysis. Due to positive nature of molecular expressions, nICA fits better to the reality of corresponding putative biological processes. In conjunction with nICA model, VIsual Statistical Data Analyzer (VISDA) is applied to group genes into modules in the latent variable...
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