2018/02/12 by Richard Y. Li, Rosa Di Felice, Remo Rohs +1 · 3 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Extreme learning machine #Machine Learning in Bioinformatics #Quantum #Quantum Computing Algorithms and Architecture #Quantum annealing #Quantum computer #Quantum machine learning #RNA and protein synthesis mechanisms #Simulated annealing #Systems biology #q-bio.GN #quant-ph
paper · pdf · doi:10.1038/s41534-018-0060-8
published as npj Quantum Information, volume 4, Article number: 14 (2018)
openalex publication_date 2018/02/12 · arxiv created 2018/02/28 · arxiv updated 2018/03/02 · openalex created_date 2018/03/29 · openalex updated_date 2026/08/06
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.