2013/12/16 by Kasper Dinkla, Dinkla, Kasper, Mohammed El-Kebir +11
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · #62P10 #76M27 #92C40 #92C42 #Bioinformatics and Genomic Networks #Cell Image Analysis Techniques #Computational Drug Discovery Methods #Computational Engineering #Computer network #Computer science #Data Structures and Algorithms (cs.DS) #Data mining #Engineering #FOS: Biological sciences #FOS: Computer and information sciences #Finance #Information retrieval #Molecular Networks (q-bio.MN) #Node (physics) #Programming language #Set (abstract data type) #Subnetwork #and Science (cs.CE) #cs.CE #cs.DS #msc:62P10 #msc:76M27 #msc:92C40 #msc:92C42 #q-bio.MN
paper · pdf · doi:10.48550/arxiv.1312.4490
arxiv created 2013/12/16 · openalex publication_date 2013/12/16 · arxiv updated 2013/12/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Background. Biological networks have growing importance for the interpretation of high-throughput "omics" data. Statistical and combinatorial methods allow to obtain mechanistic insights through the extraction of smaller subnetwork modules. Further enrichment analyses provide set-based annotations of these modules. Results. We present eXamine, a set-oriented visual analysis approach for annotated modules that displays set membership as contours on top of a node-link layout. Our approach extends upon Self Organizing Maps to simultaneously lay out nodes, links, and set contours. Conclusions. We implemented eXamine as a freely available Cytoscape app. Using eXamine we study a module that is activated by the virally-encoded G-protein coupled receptor US28 and formulate a novel hypothesis about its functioning.