Prediction of Drug–Target Interaction Using Dual-Network Integrated Logistic Matrix Factorization and Knowledge Graph Embedding
Nowadays, drug–target interactions (DTIs) prediction is a fundamental part of drug repositioning. However, on the one hand, drug–target interactions prediction models usually consider drugs or targets information, which ignore prior knowledge between drugs and targets. On the other hand, models inco...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/27/16/5131 |
| _version_ | 1797408719645966336 |
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| author | Jiaxin Li Xixin Yang Yuanlin Guan Zhenkuan Pan |
| author_facet | Jiaxin Li Xixin Yang Yuanlin Guan Zhenkuan Pan |
| author_sort | Jiaxin Li |
| collection | DOAJ |
| description | Nowadays, drug–target interactions (DTIs) prediction is a fundamental part of drug repositioning. However, on the one hand, drug–target interactions prediction models usually consider drugs or targets information, which ignore prior knowledge between drugs and targets. On the other hand, models incorporating priori knowledge cannot make interactions prediction for under-studied drugs and targets. Hence, this article proposes a novel dual-network integrated logistic matrix factorization DTIs prediction scheme (Ro-DNILMF) via a knowledge graph embedding approach. This model adds prior knowledge as input data into the prediction model and inherits the advantages of the DNILMF model, which can predict under-studied drug–target interactions. Firstly, a knowledge graph embedding model based on relational rotation (RotatE) is trained to construct the interaction adjacency matrix and integrate prior knowledge. Secondly, a dual-network integrated logistic matrix factorization prediction model (DNILMF) is used to predict new drugs and targets. Finally, several experiments conducted on the public datasets are used to demonstrate that the proposed method outperforms the single base-line model and some mainstream methods on efficiency. |
| first_indexed | 2024-03-09T04:02:36Z |
| format | Article |
| id | doaj.art-7cfebef9b1d34b2caa72bb31429cfcd1 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-09T04:02:36Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-7cfebef9b1d34b2caa72bb31429cfcd12023-12-03T14:11:05ZengMDPI AGMolecules1420-30492022-08-012716513110.3390/molecules27165131Prediction of Drug–Target Interaction Using Dual-Network Integrated Logistic Matrix Factorization and Knowledge Graph EmbeddingJiaxin Li0Xixin Yang1Yuanlin Guan2Zhenkuan Pan3College of Computer Science & Technology, Qingdao University, Qingdao 266071, ChinaCollege of Computer Science & Technology, Qingdao University, Qingdao 266071, ChinaKey Lab of Industrial Fluid Energy Conservation and Pollution Control, Ministry of Education, Qingdao University of Technology, Qingdao 266520, ChinaCollege of Computer Science & Technology, Qingdao University, Qingdao 266071, ChinaNowadays, drug–target interactions (DTIs) prediction is a fundamental part of drug repositioning. However, on the one hand, drug–target interactions prediction models usually consider drugs or targets information, which ignore prior knowledge between drugs and targets. On the other hand, models incorporating priori knowledge cannot make interactions prediction for under-studied drugs and targets. Hence, this article proposes a novel dual-network integrated logistic matrix factorization DTIs prediction scheme (Ro-DNILMF) via a knowledge graph embedding approach. This model adds prior knowledge as input data into the prediction model and inherits the advantages of the DNILMF model, which can predict under-studied drug–target interactions. Firstly, a knowledge graph embedding model based on relational rotation (RotatE) is trained to construct the interaction adjacency matrix and integrate prior knowledge. Secondly, a dual-network integrated logistic matrix factorization prediction model (DNILMF) is used to predict new drugs and targets. Finally, several experiments conducted on the public datasets are used to demonstrate that the proposed method outperforms the single base-line model and some mainstream methods on efficiency.https://www.mdpi.com/1420-3049/27/16/5131drug–target interactions predictionknowledge graph embeddingdual-network integrated logistic matrix factorization |
| spellingShingle | Jiaxin Li Xixin Yang Yuanlin Guan Zhenkuan Pan Prediction of Drug–Target Interaction Using Dual-Network Integrated Logistic Matrix Factorization and Knowledge Graph Embedding Molecules drug–target interactions prediction knowledge graph embedding dual-network integrated logistic matrix factorization |
| title | Prediction of Drug–Target Interaction Using Dual-Network Integrated Logistic Matrix Factorization and Knowledge Graph Embedding |
| title_full | Prediction of Drug–Target Interaction Using Dual-Network Integrated Logistic Matrix Factorization and Knowledge Graph Embedding |
| title_fullStr | Prediction of Drug–Target Interaction Using Dual-Network Integrated Logistic Matrix Factorization and Knowledge Graph Embedding |
| title_full_unstemmed | Prediction of Drug–Target Interaction Using Dual-Network Integrated Logistic Matrix Factorization and Knowledge Graph Embedding |
| title_short | Prediction of Drug–Target Interaction Using Dual-Network Integrated Logistic Matrix Factorization and Knowledge Graph Embedding |
| title_sort | prediction of drug target interaction using dual network integrated logistic matrix factorization and knowledge graph embedding |
| topic | drug–target interactions prediction knowledge graph embedding dual-network integrated logistic matrix factorization |
| url | https://www.mdpi.com/1420-3049/27/16/5131 |
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