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A Method of Optimizing Weight Allocation in Data Integration Based on Q-Learning for Drug-Target Interaction Prediction
Calculating and predicting drug-target interactions (DTIs) is a crucial step in the field of novel drug discovery. Nowadays, many models have improved the prediction performance of DTIs by fusing heterogeneous information, such as drug chemical structure and target protein sequence and so on. Howeve...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8959213/ https://www.ncbi.nlm.nih.gov/pubmed/35356288 http://dx.doi.org/10.3389/fcell.2022.794413 |
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author | Sun, Jiacheng Lu, You Cui, Linqian Fu, Qiming Wu, Hongjie Chen, Jianping |
author_facet | Sun, Jiacheng Lu, You Cui, Linqian Fu, Qiming Wu, Hongjie Chen, Jianping |
author_sort | Sun, Jiacheng |
collection | PubMed |
description | Calculating and predicting drug-target interactions (DTIs) is a crucial step in the field of novel drug discovery. Nowadays, many models have improved the prediction performance of DTIs by fusing heterogeneous information, such as drug chemical structure and target protein sequence and so on. However, in the process of fusion, how to allocate the weight of heterogeneous information reasonably is a huge challenge. In this paper, we propose a model based on Q-learning algorithm and Neighborhood Regularized Logistic Matrix Factorization (QLNRLMF) to predict DTIs. First, we obtain three different drug-drug similarity matrices and three different target-target similarity matrices by using different similarity calculation methods based on heterogeneous data, including drug chemical structure, target protein sequence and drug-target interactions. Then, we initialize a set of weights for the drug-drug similarity matrices and target-target similarity matrices respectively, and optimize them through Q-learning algorithm. When the optimal weights are obtained, a new drug-drug similarity matrix and a new drug-drug similarity matrix are obtained by linear combination. Finally, the drug target interaction matrix, the new drug-drug similarity matrices and the target-target similarity matrices are used as inputs to the Neighborhood Regularized Logistic Matrix Factorization (NRLMF) model for DTIs. Compared with the existing six methods of NetLapRLS, BLM-NII, WNN-GIP, KBMF2K, CMF, and NRLMF, our proposed method has achieved better effect in the four benchmark datasets, including enzymes(E), nuclear receptors (NR), ion channels (IC) and G protein coupled receptors (GPCR). |
format | Online Article Text |
id | pubmed-8959213 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89592132022-03-29 A Method of Optimizing Weight Allocation in Data Integration Based on Q-Learning for Drug-Target Interaction Prediction Sun, Jiacheng Lu, You Cui, Linqian Fu, Qiming Wu, Hongjie Chen, Jianping Front Cell Dev Biol Cell and Developmental Biology Calculating and predicting drug-target interactions (DTIs) is a crucial step in the field of novel drug discovery. Nowadays, many models have improved the prediction performance of DTIs by fusing heterogeneous information, such as drug chemical structure and target protein sequence and so on. However, in the process of fusion, how to allocate the weight of heterogeneous information reasonably is a huge challenge. In this paper, we propose a model based on Q-learning algorithm and Neighborhood Regularized Logistic Matrix Factorization (QLNRLMF) to predict DTIs. First, we obtain three different drug-drug similarity matrices and three different target-target similarity matrices by using different similarity calculation methods based on heterogeneous data, including drug chemical structure, target protein sequence and drug-target interactions. Then, we initialize a set of weights for the drug-drug similarity matrices and target-target similarity matrices respectively, and optimize them through Q-learning algorithm. When the optimal weights are obtained, a new drug-drug similarity matrix and a new drug-drug similarity matrix are obtained by linear combination. Finally, the drug target interaction matrix, the new drug-drug similarity matrices and the target-target similarity matrices are used as inputs to the Neighborhood Regularized Logistic Matrix Factorization (NRLMF) model for DTIs. Compared with the existing six methods of NetLapRLS, BLM-NII, WNN-GIP, KBMF2K, CMF, and NRLMF, our proposed method has achieved better effect in the four benchmark datasets, including enzymes(E), nuclear receptors (NR), ion channels (IC) and G protein coupled receptors (GPCR). Frontiers Media S.A. 2022-03-04 /pmc/articles/PMC8959213/ /pubmed/35356288 http://dx.doi.org/10.3389/fcell.2022.794413 Text en Copyright © 2022 Sun, Lu, Cui, Fu, Wu and Chen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Cell and Developmental Biology Sun, Jiacheng Lu, You Cui, Linqian Fu, Qiming Wu, Hongjie Chen, Jianping A Method of Optimizing Weight Allocation in Data Integration Based on Q-Learning for Drug-Target Interaction Prediction |
title | A Method of Optimizing Weight Allocation in Data Integration Based on Q-Learning for Drug-Target Interaction Prediction |
title_full | A Method of Optimizing Weight Allocation in Data Integration Based on Q-Learning for Drug-Target Interaction Prediction |
title_fullStr | A Method of Optimizing Weight Allocation in Data Integration Based on Q-Learning for Drug-Target Interaction Prediction |
title_full_unstemmed | A Method of Optimizing Weight Allocation in Data Integration Based on Q-Learning for Drug-Target Interaction Prediction |
title_short | A Method of Optimizing Weight Allocation in Data Integration Based on Q-Learning for Drug-Target Interaction Prediction |
title_sort | method of optimizing weight allocation in data integration based on q-learning for drug-target interaction prediction |
topic | Cell and Developmental Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8959213/ https://www.ncbi.nlm.nih.gov/pubmed/35356288 http://dx.doi.org/10.3389/fcell.2022.794413 |
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