background: using primary tumor gene expression has been shown to have the ability of finding metastasis-driving gene markers for the prediction of breast cancer recurrence (bcr). however, there are some difficulties associated with the analysis of microarray data which led to poor predictive power and inconsistency of the previously introduced gene signatures. methods: in this study a hybrid m...

MOTIVATION Functional interpretation of miRNA expression data is currently done in a three step procedure: select differentially expressed miRNAs, find their target genes, and carry out gene set overrepresentation analysis Nevertheless, major limitations of this approach have already been described at the gene level, while some newer arise in the miRNA scenario.Here, we propose an enhanced meth...

Recently developed gene set analysis methods evaluate differential expression patterns of gene groups instead of those of individual genes. This approach especially targets gene groups whose constituents show subtle but coordinated expression changes, which might not be detected by the usual individual gene analysis. The approach has been quite successful in deriving new information from expres...

We present a comprehensive and efficient gene set analysis tool, called 'GeneTrail' that offers a rich functionality and is easy to use. Our web-based application facilitates the statistical evaluation of high-throughput genomic or proteomic data sets with respect to enrichment of functional categories. GeneTrail covers a wide variety of biological categories and pathways, among others KEGG, TR...

Gene set enrichment analysis (GSEA) is typically based on tests derived from the KolmogorovSmirnov, which is underpowered and a need for simpler methods has been identified.[2] The wgsea package contains functions for conducting GSEA using a Wilcoxon test to test for differences in the distribution of p values between SNPs within the gene set under test and a control set of SNPs. The mean of th...

UNLABELLED Gene Ontology and other forms of gene-category analysis play a major role in the evaluation of high-throughput experiments in molecular biology. Single-category enrichment analysis procedures such as Fisher's exact test tend to flag large numbers of redundant categories as significant, which can complicate interpretation. We have recently developed an approach called model-based gene...

Gene set analysis has enhanced the microarray data analysis field with biological insights. The first introduced and widely used Over-representation analysis (ORA) method, has the limitation of the requirement of a predetermined differentially expressed genes list. To overcome this limitation, distribution based analysis (DBA) methods were developed with different analysis steps and null hypoth...

UNLABELLED TopoGSA (Topology-based Gene Set Analysis) is a web-application dedicated to the computation and visualization of network topological properties for gene and protein sets in molecular interaction networks. Different topological characteristics, such as the centrality of nodes in the network or their tendency to form clusters, can be computed and compared with those of known cellular ...

MOTIVATION Assessing functional associations between an experimentally derived gene or protein set of interest and a database of known gene/protein sets is a common task in the analysis of large-scale functional genomics data. For this purpose, a frequently used approach is to apply an over-representation-based enrichment analysis. However, this approach has four drawbacks: (i) it can only scor...

Understanding the functional implications of changes in gene expression, mutations, etc., is the aim of most genomic experiments. To achieve this, several functional profiling methods have been proposed. Such methods study the behaviour of different gene modules (e.g. gene ontology terms) in response to one particular variable (e.g. differential gene expression). In spite to the wealth of infor...