IGSA: Individual Gene Sets Analysis, including Enrichment and Clustering

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IGSA: Individual Gene Sets Analysis, including Enrichment and Clustering

Analysis of gene sets has been widely applied in various high-throughput biological studies. One weakness in the traditional methods is that they neglect the heterogeneity of genes expressions in samples which may lead to the omission of some specific and important gene sets. It is also difficult for them to reflect the severities of disease and provide expression profiles of gene sets for indi...

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Dual KS: Defining Gene Sets with Tissue Set Enrichment Analysis

BACKGROUND Gene set enrichment analysis (GSEA) is an analytic approach which simultaneously reduces the dimensionality of microarray data and enables ready inference of the biological meaning of observed gene expression patterns. Here we invert the GSEA process to identify class-specific gene signatures. Because our approach uses the Kolmogorov-Smirnov approach both to define class specific sig...

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modPhEA: model organism Phenotype Enrichment Analysis of eukaryotic gene sets

Motivation Genome-scale phenotypic data are available for many model organisms, yet existing tools to functionally interpret gene sets from these phenotypic data are largely based on mutagenesis-derived phenotypes observed in mouse or human. Results Data from both mutagenesis and knockdown experiments are incorporated into modPhEA to allow users to perform enrichment analyses based on phenoty...

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Positional gene enrichment analysis of gene sets for high-resolution identification of overrepresented chromosomal regions

The search for feature enrichment is a widely used method to characterize a set of genes. While several tools have been designed for nominal features such as Gene Ontology annotations or KEGG Pathways, very little has been proposed to tackle numerical features such as the chromosomal positions of genes. For instance, microarray studies typically generate gene lists that are differentially expre...

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Integrative Analysis of Gene Expression Data Including an Assessment of Pathway Enrichment for Predicting Prostate Cancer

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ژورنال

عنوان ژورنال: PLOS ONE

سال: 2016

ISSN: 1932-6203

DOI: 10.1371/journal.pone.0164542