ArticleBioinformatics advances2025
QOMIC: quantum optimization for motif identification.
Article in Bioinformatics advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 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
- A foundational quantum framework for multi-pattern string matching in k-mer detection.Frontiers in bioinformatics · 2026Article
- Quantum implementation of multi-pattern string matching for k-mer detection.bioRxiv : the preprint server for biology · 2025Article
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Authors and funding
4 authors.
Funding
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Abstract
Motivation: Network motif identification (MI) problem aims to find topological patterns in biological networks. Identifying disjoint motifs is a computationally challenging problem using classical computers. Quantum computers enable solving high complexity problems which do not scale using classical computers. In this article, we develop the first quantum solution, called QOMIC (Quantum Optimization for Motif IdentifiCation), to the MI problem. QOMIC transforms the MI problem using a integer model, which serves as the foundation to develop our quantum solution. We develop and implement the quantum circuit to find motif locations in the given network using this model. Results: Our experiments demonstrate that QOMIC outperforms the existing solutions developed for the classical computer, in term of motif counts. We also observe that QOMIC can efficiently find motifs in human regulatory networks associated with five neurodegenerative diseases: Alzheimer's, Parkinson's, Huntington's, Amyotrophic Lateral Sclerosis, and Motor Neurone Disease. Availability and implementation: Our implementation can be found in https://github.com/ngominhhoang/Quantum-Motif-Identification.git.
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Registered trials
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