Claire Simpson - Cell Signaling Technology
Presentation: https://www.slideshare.net/neo4j/analyzing-perturbed-coexpression-networks-in-cancer-using-a-graph-database
We used the graph database management system Neo4j to store and analyze co-expression networks derived from RNAseq data from The Cancer Genome Atlas (TCGA). Comparing co-expression in tumors versus healthy tissues in six cancer types revealed significant perturbation tracing back to erroneous or rewired gene regulation. Applying centrality, community detection, and pathfinding graph algorithms uncovered destruction or creation of central nodes, modules, and relationships in co-expression networks of tumors. Given the speed, accuracy and straightforwardness of managing these densely connected networks, we conclude that graph databases are ready for entering the arena of biological data.

Our third Neo4j Health Care & Life Sciences Workshop has been set up to showcase practical solutions to common problems as well as helping to incubate collaboration, innovation and good practice. Graph databases are powerful tools that are inherently capable of managing vast quantities of data points and the web of relationships between them. As people start turning to tools like Neo4j for answers there are inevitably more questions: data modeling, performance, resilience, interoperability. These are the kinds of questions we want to help you answer.

Learn more: https://development.neo4j.dev/use-cases/life-sciences/