RingNet: An Interactive Platform for Multi-Modal Data Visualization in Networks

The exponential growth of multi-omics datasets in systems medicine has created an urgent need for intuitive visualization tools. These tools must be able to effectively represent complex biological networks and remain accessible to domain experts without extensive computational training. Current network visualization approaches often require specialized programming skills and/or cannot handle the scale and complexity of modern biomedical datasets, which creates significant barriers to biological discovery. We develop RingNet, a web-based interactive visualization tool that integrates computational efficiency with flexible, user-driven exploration. The tool meets the communitys need to visualize multi-modal datasets within a single, compact network representation. RingNet uses an R backend for network computation and coordinate optimization. This generates JSON data structures that feed into a JavaScript and HTML frontend, which provides real-time, interactive visualization functions. It offers dynamic layout adjustments, node and edge filtering, and customizable color schemes for representing data. It can export reproducible, publication-ready figures in SVG and PDF formats. In our case studies, we visualize a gene regulatory network in breast cancer and a cell-to-cell communication network in atopic dermatitis. This demonstrates RingNets ability to reveal biological relationships across multiple data modalities. RingNet lowers the barrier to exploring, analyzing, and communicating network medicine findings, thereby accelerating research. Graphical AbstractRingNet is a tool for visualizing multi-modal data in networks. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=69 SRC="FIGDIR/small/700593v2_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@bbe4a5org.highwire.dtl.DTLVardef@1a033feorg.highwire.dtl.DTLVardef@b58b61org.highwire.dtl.DTLVardef@d1b398_HPS_FORMAT_FIGEXP M_FIG C_FIG Key MessagesO_LIRingNet enables intuitive, interactive visualization of multiomics networks without requiring advanced computational or programming expertise. C_LIO_LIRingNet integrates efficient backend computation with real-time, flexible frontend exploration in a single, web-based framework. C_LIO_LIRingNet reveals cross-modal biological relationships and produces reproducible, publication-ready figures that accelerate research. C_LI