ArticleNPJ quantum information2018
Quantum annealing versus classical machine learning applied to a simplified computational biology problem.
Article in NPJ quantum information, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Quantum computing in bioinformatics: a systematic review mapping.Briefings in bioinformatics · 2024Pooled it
- Quantum computing and the implementation of precision medicine.NPJ genomic medicine · 2025Review
- Quantum annealing-based route optimization for commercial AGV operating systems in large-scale logistics warehouses.Scientific reports · 2025Article
- Hybrid quantum-classical stochastic programming for co-planning 5G base stations and photovoltaic power stations in urban communities.Scientific reports · 2025Article
- QTFPred: robust high-performance quantum machine learning modeling that predicts main and cooperative transcription factor bindings with base resolution.Briefings in bioinformatics · 2025Article
- Efficient optimization accelerator framework for multi-state spin Ising problems.Nature communications · 2025Article
- Ascertaining Susceptibilities in Smart Contracts: A Quantum Machine Learning Approach.Entropy (Basel, Switzerland) · 2025Article
- A primer for quantum computing and its applications to healthcare and biomedical research.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Investigation of factors regarding the effects of COVID-19 pandemic on college students' depression by quantum annealer.Scientific reports · 2024Article
- Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics.Molecular biotechnology · 2024Review
- Quantum computing on nucleic acid research: Approaching towards next-generation computing.Molecular therapy. Nucleic acids · 2023Article
- Prediction of cooperative homeodomain DNA binding sites from high-throughput-SELEX data.Nucleic acids research · 2023Article
- Quantum Annealing Boosts Prediction of Multimolecular Adsorption on Solid Surfaces Avoiding Combinatorial Explosion.JACS Au · 2023Article
- Programming Quantum Neural Networks on NISQ Systems: An Overview of Technologies and Methodologies.Entropy (Basel, Switzerland) · 2023Article
- Quantum annealing for microstructure equilibration with long-range elastic interactions.Scientific reports · 2023Article
- Highly reconfigurable oscillator-based Ising Machine through quasiperiodic modulation of coupling strength.Scientific reports · 2023Article
- Article
- Quantum computing algorithms: getting closer to critical problems in computational biology.Briefings in bioinformatics · 2022Review
- Biology and medicine in the landscape of quantum advantages.Journal of the Royal Society, Interface · 2022Review
- A new method of software vulnerability detection based on a quantum neural network.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Transcription factors regulate gene expression, but how these proteins recognize and specifically bind to their DNA targets is still debated. Machine learning models are effective means to reveal interaction mechanisms. Here we studied the ability of a quantum machine learning approach to predict binding specificity. Using simplified datasets of a small number of DNA sequences derived from actual binding affinity experiments, we trained a commercially available quantum annealer to classify and rank transcription factor binding. The results were compared to state-of-the-art classical approaches for the same simplified datasets, including simulated annealing, simulated quantum annealing, multiple linear regression, LASSO, and extreme gradient boosting. Despite technological limitations, we find a slight advantage in classification performance and nearly equal ranking performance using the quantum annealer for these fairly small training data sets. Thus, we propose that quantum annealing might be an effective method to implement machine learning for certain computational biology problems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.