Open Data for Differential Network Analysis in Glioma.

ABSTRACT: The complexity of cancer diseases demands bioinformatic techniques and translational research based on big data and personalized medicine. Open data enables researchers to accelerate cancer studies, save resources and foster collaboration. Several tools and programming approaches are available for analyzing data, including annotation, clustering, comparison and extrapolation, merging, enrichment, functional association and statistics. We exploit openly available data via cancer gene expression analysis, we apply refinement as well as enrichment analysis via gene ontology and conclude with graph-based visualization of involved protein interaction networks as a basis for signaling. The different databases allowed for the construction of huge networks or specified ones consisting of high-conf

SUBMITTER: Jean-Quartier C 

PROVIDER: S-EPMC7013918 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature