# ID-GBA: Subgraph Extension With Information Distance Guilt by Association in Complex Networks

## TL;DR

This paper introduces ID-GBA, a new method for expanding disease-related gene clusters in complex networks, which outperforms existing tools in identifying known disease genes.

## Contribution

The novel ID-GBA algorithm uses information distance and guilt-by-association with an automated thresholding mechanism for subgraph extension.

## Key findings

- ID-GBA outperforms Random Walk with Restarts and Personalized PageRank in capturing known disease genes with higher NDCG scores.
- The method successfully expands disease clusters and recaptures known disease genes in nine disease/control gene expression networks.
- ID-GBA includes an automated, data-driven thresholding mechanism, eliminating the need for user-specified parameters.

## Abstract

Here, we introduce the ID-GBA (Information Distance Guilt By Association) method to expand highly connected sets of nodes by deploying a novel algorithm for subgraph extension based on the guilt-by-association principle and information distance. In this study, ID-GBA was utilized to expand disease clusters, and identify novel disease genes. We first validate its ability to expand related disease sets from disease/disease graphs built using Open Targets’ gene association scores. We then analyze disease/control gene expression networks and show that ID-GBA recaptures known disease genes in nine disease/control graphs. Compared to existing methods such as Random Walk with Restarts and Personalized PageRank, ID-GBA achieves significantly higher Normalized Discounted Cumulative Gain scores, which indicates superior predictive performance at capturing known disease genes. Additionally, unlike other approaches that require users to specify either a threshold parameter or a fixed number of nodes to include in the extended subgraph, ID-GBA includes a built-in, automated, and data-driven thresholding mechanism. These results establish ID-GBA as a novel open-source tool to uncover hidden relationships in gene/gene, disease/disease, and other complex networks.