Integrative approach for predicting drug-target interactions via matrix factorization and broad learning systems
In the drug discovery process, time and costs are the most typical problems resulting from the experimental screening of drug-target interactions (DTIs). To address these limitations, many computational methods have been developed to achieve more accurate predictions. However, identifying DTIs mostl...
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AIMS Press
2024-01-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2024115?viewType=HTML |
| _version_ | 1797302516985102336 |
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| author | Wanying Xu Xixin Yang Yuanlin Guan Xiaoqing Cheng Yu Wang |
| author_facet | Wanying Xu Xixin Yang Yuanlin Guan Xiaoqing Cheng Yu Wang |
| author_sort | Wanying Xu |
| collection | DOAJ |
| description | In the drug discovery process, time and costs are the most typical problems resulting from the experimental screening of drug-target interactions (DTIs). To address these limitations, many computational methods have been developed to achieve more accurate predictions. However, identifying DTIs mostly rely on separate learning tasks with drug and target features that neglect interaction representation between drugs and target. In addition, the lack of these relationships may lead to a greatly impaired performance on the prediction of DTIs. Aiming at capturing comprehensive drug-target representations and simplifying the network structure, we propose an integrative approach with a convolution broad learning system for the DTI prediction (ConvBLS-DTI) to reduce the impact of the data sparsity and incompleteness. First, given the lack of known interactions for the drug and target, the weighted K-nearest known neighbors (WKNKN) method was used as a preprocessing strategy for unknown drug-target pairs. Second, a neighborhood regularized logistic matrix factorization (NRLMF) was applied to extract features of updated drug-target interaction information, which focused more on the known interaction pair parties. Then, a broad learning network incorporating a convolutional neural network was established to predict DTIs, which can make classification more effective using a different perspective. Finally, based on the four benchmark datasets in three scenarios, the ConvBLS-DTI's overall performance out-performed some mainstream methods. The test results demonstrate that our model achieves improved prediction effect on the area under the receiver operating characteristic curve and the precision-recall curve. |
| first_indexed | 2024-03-07T23:38:58Z |
| format | Article |
| id | doaj.art-0dd46e54386543b191ea87ca15869cb9 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-03-07T23:38:58Z |
| publishDate | 2024-01-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-0dd46e54386543b191ea87ca15869cb92024-02-20T01:18:43ZengAIMS PressMathematical Biosciences and Engineering1551-00182024-01-012122608262510.3934/mbe.2024115Integrative approach for predicting drug-target interactions via matrix factorization and broad learning systemsWanying Xu0Xixin Yang1Yuanlin Guan2Xiaoqing Cheng 3Yu Wang 41. College of Computer Science & Technology, Qingdao University, Qingdao 266071, China1. College of Computer Science & Technology, Qingdao University, Qingdao 266071, China 2. School of Automation, Qingdao University, Qingdao 266071, China3. Key Lab of Industrial Fluid Energy Conservation and Pollution Control, Ministry of Education, Qingdao University of Technology, Qingdao 266520, China 4. School of Mechanical & Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China1. College of Computer Science & Technology, Qingdao University, Qingdao 266071, China1. College of Computer Science & Technology, Qingdao University, Qingdao 266071, ChinaIn the drug discovery process, time and costs are the most typical problems resulting from the experimental screening of drug-target interactions (DTIs). To address these limitations, many computational methods have been developed to achieve more accurate predictions. However, identifying DTIs mostly rely on separate learning tasks with drug and target features that neglect interaction representation between drugs and target. In addition, the lack of these relationships may lead to a greatly impaired performance on the prediction of DTIs. Aiming at capturing comprehensive drug-target representations and simplifying the network structure, we propose an integrative approach with a convolution broad learning system for the DTI prediction (ConvBLS-DTI) to reduce the impact of the data sparsity and incompleteness. First, given the lack of known interactions for the drug and target, the weighted K-nearest known neighbors (WKNKN) method was used as a preprocessing strategy for unknown drug-target pairs. Second, a neighborhood regularized logistic matrix factorization (NRLMF) was applied to extract features of updated drug-target interaction information, which focused more on the known interaction pair parties. Then, a broad learning network incorporating a convolutional neural network was established to predict DTIs, which can make classification more effective using a different perspective. Finally, based on the four benchmark datasets in three scenarios, the ConvBLS-DTI's overall performance out-performed some mainstream methods. The test results demonstrate that our model achieves improved prediction effect on the area under the receiver operating characteristic curve and the precision-recall curve.https://www.aimspress.com/article/doi/10.3934/mbe.2024115?viewType=HTMLdrug-target interaction predictionbroad learning systemneighbor regularization logistic matrix factorization |
| spellingShingle | Wanying Xu Xixin Yang Yuanlin Guan Xiaoqing Cheng Yu Wang Integrative approach for predicting drug-target interactions via matrix factorization and broad learning systems Mathematical Biosciences and Engineering drug-target interaction prediction broad learning system neighbor regularization logistic matrix factorization |
| title | Integrative approach for predicting drug-target interactions via matrix factorization and broad learning systems |
| title_full | Integrative approach for predicting drug-target interactions via matrix factorization and broad learning systems |
| title_fullStr | Integrative approach for predicting drug-target interactions via matrix factorization and broad learning systems |
| title_full_unstemmed | Integrative approach for predicting drug-target interactions via matrix factorization and broad learning systems |
| title_short | Integrative approach for predicting drug-target interactions via matrix factorization and broad learning systems |
| title_sort | integrative approach for predicting drug target interactions via matrix factorization and broad learning systems |
| topic | drug-target interaction prediction broad learning system neighbor regularization logistic matrix factorization |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2024115?viewType=HTML |
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