Quantum annealing versus classical machine learning applied to a simplified computational biology problem

Quantum annealing versus classical machine learning applied to a simplified computational biology... 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 classify and rank binding affinities. Using simplified data sets 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 data sets, 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png npj Quantum Information Springer Journals

Quantum annealing versus classical machine learning applied to a simplified computational biology problem

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Publisher
Nature Publishing Group UK
Copyright
Copyright © 2018 by The Author(s)
Subject
Physics; Physics, general; Quantum Physics; Quantum Information Technology, Spintronics; Quantum Computing; Quantum Field Theories, String Theory; Classical and Quantum Gravitation, Relativity Theory
eISSN
2056-6387
D.O.I.
10.1038/s41534-018-0060-8
Publisher site
See Article on Publisher Site

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 classify and rank binding affinities. Using simplified data sets 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 data sets, 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.

Journal

npj Quantum InformationSpringer Journals

Published: Feb 21, 2018

References

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