# Measurement of Disease Comorbidity Using Semantic Profiling of Disease Genes

## TL;DR

This paper introduces a new method to measure disease comorbidity by analyzing similarities in gene sets, even when diseases share no common genes.

## Contribution

The novel contribution is GS.CoMoD, a gene-set-based approach to detect comorbidity without requiring overlapping genes.

## Key findings

- GS.CoMoD achieved higher scores for comorbid disease pairs compared to random pairs in simulations.
- GS.CoMoD outperformed existing methods in detecting disease comorbidity.
- Comorbidity can be inferred from similarities in gene-set enrichment p-values even without shared genes.

## Abstract

The identification of overlapping disease genes between different diseases is the first step in the elucidation of the biological mechanism of disease comorbidity; however, in the absence of common genes, it is difficult to determine the mechanism of comorbidity even if clinical evidence of disease co-occurrence exists. In this research, a gene-set-based measurement of the comorbidity of diseases (GS.CoMoD) was proposed. The underlying assumption of GS.CoMoD is that if the p-value vectors obtained from the enrichment analyses of different disease gene lists indicate similarity, the diseases are possibly comorbid. Therefore, comorbidity can be detected even without overlapping genes. A simulation analysis showed that GS.CoMoD yielded higher scores for comorbid disease pairs vs. random disease pairs. Moreover, comparison analyses revealed that GS.CoMoD outperformed the pre-existing methods for detecting comorbidity.