ArticleBioinformatics (Oxford, England)2024
Disease gene prioritization with quantum walks.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Quantum bioinformatics: a systematic review of methods, trends, and challenges.Briefings in bioinformatics · 2026Pooled it
- The exposome and inflammatory bowel disease: a framework for primary and primordial prevention.Nature reviews. Gastroenterology & hepatology · 2026Review
- Advancing single-cell omics and cell-based therapeutics with quantum computing.Nature reviews. Molecular cell biology · 2026Review
- Advancing understanding of long COVID pathophysiology through quantum walk-based network analysis.Bioinformatics advances · 2026Article
- Article
- Coalition of explainable artificial intelligence and quantum computing in precision medicine.Computational and structural biotechnology journal · 2025Review
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Authors and funding
5 authors.
Funding
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Abstract
motivationDisease gene prioritization methods assign scores to genes or proteins according to their likely relevance for a given disease based on a provided set of seed genes. This scoring can be used to find new biologically relevant genes or proteins for many diseases. Although methods based on classical random walks have proven to yield competitive results, quantum walk methods have not been explored to this end.
resultsWe propose a new algorithm for disease gene prioritization based on continuous-time quantum walks using the adjacency matrix of a protein-protein interaction (PPI) network. We demonstrate the success of our proposed quantum walk 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 of recall and average precision values. We further validate our method by performing an enrichment analysis of the predicted genes for coronary artery disease. AVAILABILITY AND IMPLEMENTATION: The data and code for the methods can be accessed at https://github.com/markgolds/qdgp.
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