HCNov 13, 2017

PRE-render Content Using Tiles (PRECUT). 1. Large-Scale Compound-Target Relationship Analyses

arXiv:1711.06328v11 citations
Originality Synthesis-oriented
AI Analysis

This work addresses the problem of slow network visualization for researchers in cheminformatics, though it appears incremental as it adapts existing tiling techniques to a specific domain.

The paper tackles the computational challenge of visualizing complex networks by introducing PRECUT, a method that pre-renders networks into tiles at different zoom levels, enabling efficient navigation through tile delivery. It demonstrates this approach by applying it to large-scale compound-target relationship analyses, specifically creating and visualizing matched molecular pair networks using data from the ChEMBL database.

Visualizing a complex network is computationally intensive process and depends heavily on the number of components in the network. One way to solve this problem is not to render the network in real time. PRE-render Content Using Tiles (PRECUT) is a process to convert any complex network into a pre-rendered network. Tiles are generated from pre-rendered images at different zoom levels, and navigating the network simply becomes delivering relevant tiles. PRECUT is exemplified by performing large-scale compound-target relationship analyses. Matched molecular pair (MMP) networks were created using compounds and the target class description found in the ChEMBL database. To visualize MMP networks, the MMP network viewer has been implemented in COMBINE and as a web application, hosted at http://cheminformatic.com/mmpnet/.

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