CancerLinker: Explorations of Cancer Study Network

TL;DR

CancerLinker is a visual analytic tool that merges data from multiple cancer studies to explore bio-molecule relationships, identify common proteins, and analyze patient profiles, aiding cancer research and understanding complex interactions.

Contribution

The paper introduces CancerLinker, a novel visualization platform that integrates data from 26 cancer studies to facilitate exploration of protein interactions and patient data in cancer research.

Findings

Merged 26 cancer studies into a unified network.

Visualized common proteins using bubble graphs.

Highlighted patient profile patterns with parallel coordinates.

Abstract

Interactive visualization tools are highly desirable to biologist and cancer researchers to explore the complex structures, detect patterns and find out the relationships among bio-molecules responsible for a cancer type. A pathway contains various bio-molecules in different layers of the cell which is responsible for specific cancer type. Researchers are highly interested in understanding the relationships among the proteins of different pathways and furthermore want to know how those proteins are interacting in different pathways for various cancer types. Biologists find it useful to merge the data of different cancer studies in a single network and see the relationships among the different proteins which can help them detect the common proteins in cancer studies and hence reveal the pattern of interactions of those proteins. We introduce the CancerLinker, a visual analytic tool that helps researchers explore cancer study interaction network. Twenty-six cancer studies are merged to explore pathway data and bio-molecules relationships that can provide the answers to some significant questions which are helpful in cancer research. The CancerLinker also helps biologists explore the critical mutated proteins in multiple cancer studies. A bubble graph is constructed to visualize common protein based on its frequency and biological assemblies. Parallel coordinates highlight patterns of patient profiles (obtained from cBioportal by WebAPI services) on different attributes for a specified cancer study