Gene set analysis methods are widely used to provide insight into high-throughput gene
expression data. There are many gene set analysis methods available. These methods rely …

The rapidly expanding field of big data analytics has started to play a pivotal role in the
evolution of healthcare practices and research. It has provided tools to accumulate, manage …

Abstract The Reactome Knowledgebase (https://reactome. org) provides molecular details of
signal transduction, transport, DNA replication, metabolism, and other cellular processes as …

Background Reactome aims to provide bioinformatics tools for visualisation, interpretation
and analysis of pathway knowledge to support basic research, genome analysis, modelling …

Background Many high-throughput experiments compare two phenotypes such as disease
vs. healthy, with the goal of understanding the underlying biological phenomena …

Background Gene-set enrichment analysis is a useful technique to help functionally
characterize large gene lists, such as the results of gene expression experiments. This …

Pathway analysis has become the first choice for gaining insight into the underlying biology
of differentially expressed genes and proteins, as it reduces complexity and has increased …

DNA microarrays can be used to identify gene expression changes characteristic of human
disease. This is challenging, however, when relevant differences are subtle at the level of …

Over-representation analysis (ORA) is one of the commonest pathway analysis approaches
used for the functional interpretation of metabolomics datasets. Despite the widespread use …

Gene set enrichment tests (aka functional enrichment analysis) are among the most
frequently used methods in computational biology. Despite this popularity, there are …