# Disease Gene Prioritization With Quantum Walks

arXiv: https://arxiv.org/abs/2311.05486 · HTML: https://mlsanity.com/paper/2311.05486/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 66 references that are in the map.
- First posted on arXiv 9 Nov 2023.

## Builds on (most cited first, 4 of 4)

- [Random Walks: A Review of Algorithms and Applications](https://mlsanity.com/paper/2008.03639/) (2020)
- [Large-scale analysis of disease pathways in the human interactome](https://mlsanity.com/paper/1712.00843/) (2017)
- [Quantum Link Prediction in Complex Networks](https://mlsanity.com/paper/2112.04768/) (2021)
- [Link prediction with continuous-time classical and quantum walks](https://mlsanity.com/paper/2208.11030/) (2022)

## Built on by (most cited first, 1 of 1)

- [VQIL: Visible Quantum Interference Learning for Perturbation Response Ranking on Graphs](https://mlsanity.com/paper/2610.13689/) (2026)

## Abstract (arXiv)

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.