Disease Gene Prioritization With Quantum Walks

TL;DR

This paper introduces a quantum walk-based algorithm for disease gene prioritization that outperforms existing methods by leveraging quantum properties and seed node self-loops within protein-protein interaction networks.

Contribution

The paper presents a novel quantum walk algorithm for gene prioritization that improves prediction accuracy and incorporates seed self-loops for enhanced performance.

Findings

Higher performance in predicting disease genes compared to previous methods

Effective use of seed self-loops to maintain locality in the quantum walk

Successful validation through cross-validation and enrichment analysis

Abstract

Disease gene prioritization assigns scores to genes or proteins according to their likely relevance for a given disease based on a provided set of seed genes. Here, we describe a new algorithm for disease gene prioritization based on continuous-time quantum walks using the adjacency matrix of a protein-protein interaction (PPI) network. Our algorithm can be seen as a quantum version of a previous method known as the diffusion kernel, but, importantly, has higher performance in predicting disease genes, and also permits the encoding of seed node self-loops into the underlying Hamiltonian, which offers yet another boost in performance. We demonstrate the success of our proposed method by comparing it to several well-known gene prioritization methods on three disease sets, across seven different PPI networks. In order to compare these methods, we use cross-validation and examine the mean reciprocal ranks and recall values. We further validate our method by performing an enrichment analysis of the predicted genes for coronary artery disease. We also investigate the impact of adding self-loops to the seeds, and argue that they allow the quantum walker to remain more local to low-degree seed nodes.