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Hybrid Quantum Neural Network for Drug Response Prediction
SIMPLE SUMMARY: This work successfully employs a novel approach in processing patient and drug data to predict the drug response for cancer patients. The approach uses a deep quantum computing circuit as part of a machine learning architecture to simultaneously consider the cell line and the chemica...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10216276/ https://www.ncbi.nlm.nih.gov/pubmed/37345042 http://dx.doi.org/10.3390/cancers15102705 |
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author | Sagingalieva, Asel Kordzanganeh, Mohammad Kenbayev, Nurbolat Kosichkina, Daria Tomashuk, Tatiana Melnikov, Alexey |
author_facet | Sagingalieva, Asel Kordzanganeh, Mohammad Kenbayev, Nurbolat Kosichkina, Daria Tomashuk, Tatiana Melnikov, Alexey |
author_sort | Sagingalieva, Asel |
collection | PubMed |
description | SIMPLE SUMMARY: This work successfully employs a novel approach in processing patient and drug data to predict the drug response for cancer patients. The approach uses a deep quantum computing circuit as part of a machine learning architecture to simultaneously consider the cell line and the chemical and predict its effect. The resultant hybrid quantum architecture predicted the drug response with 15% better effectiveness than its classical counterpart. This result presents a step towards designing personalized drugs using the abilities of quantum computers. ABSTRACT: Cancer is one of the leading causes of death worldwide. It is caused by various genetic mutations, which makes every instance of the disease unique. Since chemotherapy can have extremely severe side effects, each patient requires a personalized treatment plan. Finding the dosages that maximize the beneficial effects of the drugs and minimize their adverse side effects is vital. Deep neural networks automate and improve drug selection. However, they require a lot of data to be trained on. Therefore, there is a need for machine-learning approaches that require less data. Hybrid quantum neural networks were shown to provide a potential advantage in problems where training data availability is limited. We propose a novel hybrid quantum neural network for drug response prediction based on a combination of convolutional, graph convolutional, and deep quantum neural layers of 8 qubits with 363 layers. We test our model on the reduced Genomics of Drug Sensitivity in Cancer dataset and show that the hybrid quantum model outperforms its classical analog by 15% in predicting [Formula: see text] drug effectiveness values. The proposed hybrid quantum machine learning model is a step towards deep quantum data-efficient algorithms with thousands of quantum gates for solving problems in personalized medicine, where data collection is a challenge. |
format | Online Article Text |
id | pubmed-10216276 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102162762023-05-27 Hybrid Quantum Neural Network for Drug Response Prediction Sagingalieva, Asel Kordzanganeh, Mohammad Kenbayev, Nurbolat Kosichkina, Daria Tomashuk, Tatiana Melnikov, Alexey Cancers (Basel) Article SIMPLE SUMMARY: This work successfully employs a novel approach in processing patient and drug data to predict the drug response for cancer patients. The approach uses a deep quantum computing circuit as part of a machine learning architecture to simultaneously consider the cell line and the chemical and predict its effect. The resultant hybrid quantum architecture predicted the drug response with 15% better effectiveness than its classical counterpart. This result presents a step towards designing personalized drugs using the abilities of quantum computers. ABSTRACT: Cancer is one of the leading causes of death worldwide. It is caused by various genetic mutations, which makes every instance of the disease unique. Since chemotherapy can have extremely severe side effects, each patient requires a personalized treatment plan. Finding the dosages that maximize the beneficial effects of the drugs and minimize their adverse side effects is vital. Deep neural networks automate and improve drug selection. However, they require a lot of data to be trained on. Therefore, there is a need for machine-learning approaches that require less data. Hybrid quantum neural networks were shown to provide a potential advantage in problems where training data availability is limited. We propose a novel hybrid quantum neural network for drug response prediction based on a combination of convolutional, graph convolutional, and deep quantum neural layers of 8 qubits with 363 layers. We test our model on the reduced Genomics of Drug Sensitivity in Cancer dataset and show that the hybrid quantum model outperforms its classical analog by 15% in predicting [Formula: see text] drug effectiveness values. The proposed hybrid quantum machine learning model is a step towards deep quantum data-efficient algorithms with thousands of quantum gates for solving problems in personalized medicine, where data collection is a challenge. MDPI 2023-05-10 /pmc/articles/PMC10216276/ /pubmed/37345042 http://dx.doi.org/10.3390/cancers15102705 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Sagingalieva, Asel Kordzanganeh, Mohammad Kenbayev, Nurbolat Kosichkina, Daria Tomashuk, Tatiana Melnikov, Alexey Hybrid Quantum Neural Network for Drug Response Prediction |
title | Hybrid Quantum Neural Network for Drug Response Prediction |
title_full | Hybrid Quantum Neural Network for Drug Response Prediction |
title_fullStr | Hybrid Quantum Neural Network for Drug Response Prediction |
title_full_unstemmed | Hybrid Quantum Neural Network for Drug Response Prediction |
title_short | Hybrid Quantum Neural Network for Drug Response Prediction |
title_sort | hybrid quantum neural network for drug response prediction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10216276/ https://www.ncbi.nlm.nih.gov/pubmed/37345042 http://dx.doi.org/10.3390/cancers15102705 |
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