Hybrid Quantum Neural Network for Drug Response Prediction
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 b...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
|
| Series: | Cancers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-6694/15/10/2705 |
| _version_ | 1797600745486286848 |
|---|---|
| author | Asel Sagingalieva Mohammad Kordzanganeh Nurbolat Kenbayev Daria Kosichkina Tatiana Tomashuk Alexey Melnikov |
| author_facet | Asel Sagingalieva Mohammad Kordzanganeh Nurbolat Kenbayev Daria Kosichkina Tatiana Tomashuk Alexey Melnikov |
| author_sort | Asel Sagingalieva |
| collection | DOAJ |
| description | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>IC</mi><mn>50</mn></msub></semantics></math></inline-formula> 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. |
| first_indexed | 2024-03-11T03:53:18Z |
| format | Article |
| id | doaj.art-f1c132d162b8453a89d33d3986e7a316 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-11T03:53:18Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-f1c132d162b8453a89d33d3986e7a3162023-11-18T00:47:45ZengMDPI AGCancers2072-66942023-05-011510270510.3390/cancers15102705Hybrid Quantum Neural Network for Drug Response PredictionAsel Sagingalieva0Mohammad Kordzanganeh1Nurbolat Kenbayev2Daria Kosichkina3Tatiana Tomashuk4Alexey Melnikov5Terra Quantum AG, Kornhausstrasse 25, 9000 St. Gallen, SwitzerlandTerra Quantum AG, Kornhausstrasse 25, 9000 St. Gallen, SwitzerlandTerra Quantum AG, Kornhausstrasse 25, 9000 St. Gallen, SwitzerlandTerra Quantum AG, Kornhausstrasse 25, 9000 St. Gallen, SwitzerlandTerra Quantum AG, Kornhausstrasse 25, 9000 St. Gallen, SwitzerlandTerra Quantum AG, Kornhausstrasse 25, 9000 St. Gallen, SwitzerlandCancer 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>IC</mi><mn>50</mn></msub></semantics></math></inline-formula> 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.https://www.mdpi.com/2072-6694/15/10/2705precision oncologydrug response predictionhybrid quantum machine learningquantum computing in healthcare |
| spellingShingle | Asel Sagingalieva Mohammad Kordzanganeh Nurbolat Kenbayev Daria Kosichkina Tatiana Tomashuk Alexey Melnikov Hybrid Quantum Neural Network for Drug Response Prediction Cancers precision oncology drug response prediction hybrid quantum machine learning quantum computing in healthcare |
| 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 | precision oncology drug response prediction hybrid quantum machine learning quantum computing in healthcare |
| url | https://www.mdpi.com/2072-6694/15/10/2705 |
| work_keys_str_mv | AT aselsagingalieva hybridquantumneuralnetworkfordrugresponseprediction AT mohammadkordzanganeh hybridquantumneuralnetworkfordrugresponseprediction AT nurbolatkenbayev hybridquantumneuralnetworkfordrugresponseprediction AT dariakosichkina hybridquantumneuralnetworkfordrugresponseprediction AT tatianatomashuk hybridquantumneuralnetworkfordrugresponseprediction AT alexeymelnikov hybridquantumneuralnetworkfordrugresponseprediction |