Heterogeneous network propagation with forward similarity integration to enhance drug–target association prediction
Identification of drug–target interaction (DTI) is a crucial step to reduce time and cost in the drug discovery and development process. Since various biological data are publicly available, DTIs have been identified computationally. To predict DTIs, most existing methods focus on a single similarit...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2022-10-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1124.pdf |
| _version_ | 1811338915577069568 |
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| author | Piyanut Tangmanussukum Thitipong Kawichai Apichat Suratanee Kitiporn Plaimas |
| author_facet | Piyanut Tangmanussukum Thitipong Kawichai Apichat Suratanee Kitiporn Plaimas |
| author_sort | Piyanut Tangmanussukum |
| collection | DOAJ |
| description | Identification of drug–target interaction (DTI) is a crucial step to reduce time and cost in the drug discovery and development process. Since various biological data are publicly available, DTIs have been identified computationally. To predict DTIs, most existing methods focus on a single similarity measure of drugs and target proteins, whereas some recent methods integrate a particular set of drug and target similarity measures by a single integration function. Therefore, many DTIs are still missing. In this study, we propose heterogeneous network propagation with the forward similarity integration (FSI) algorithm, which systematically selects the optimal integration of multiple similarity measures of drugs and target proteins. Seven drug–drug and nine target–target similarity measures are applied with four distinct integration methods to finally create an optimal heterogeneous network model. Consequently, the optimal model uses the target similarity based on protein sequences and the fused drug similarity, which combines the similarity measures based on chemical structures, the Jaccard scores of drug–disease associations, and the cosine scores of drug–drug interactions. With an accuracy of 99.8%, this model significantly outperforms others that utilize different similarity measures of drugs and target proteins. In addition, the validation of the DTI predictions of this model demonstrates the ability of our method to discover missing potential DTIs. |
| first_indexed | 2024-04-13T18:17:46Z |
| format | Article |
| id | doaj.art-35797d7ff0b440fd814035e9fc4ade08 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-04-13T18:17:46Z |
| publishDate | 2022-10-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-35797d7ff0b440fd814035e9fc4ade082022-12-22T02:35:36ZengPeerJ Inc.PeerJ Computer Science2376-59922022-10-018e112410.7717/peerj-cs.1124Heterogeneous network propagation with forward similarity integration to enhance drug–target association predictionPiyanut Tangmanussukum0Thitipong Kawichai1Apichat Suratanee2Kitiporn Plaimas3Advanced Virtual and Intelligent Computing (AVIC) Center, Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, ThailandDepartment of Mathematics and Computer Science, Academic Division, Chulachomklao Royal Military Academy, Nakhon Nayok, ThailandDepartment of Mathematics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok, ThailandAdvanced Virtual and Intelligent Computing (AVIC) Center, Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, ThailandIdentification of drug–target interaction (DTI) is a crucial step to reduce time and cost in the drug discovery and development process. Since various biological data are publicly available, DTIs have been identified computationally. To predict DTIs, most existing methods focus on a single similarity measure of drugs and target proteins, whereas some recent methods integrate a particular set of drug and target similarity measures by a single integration function. Therefore, many DTIs are still missing. In this study, we propose heterogeneous network propagation with the forward similarity integration (FSI) algorithm, which systematically selects the optimal integration of multiple similarity measures of drugs and target proteins. Seven drug–drug and nine target–target similarity measures are applied with four distinct integration methods to finally create an optimal heterogeneous network model. Consequently, the optimal model uses the target similarity based on protein sequences and the fused drug similarity, which combines the similarity measures based on chemical structures, the Jaccard scores of drug–disease associations, and the cosine scores of drug–drug interactions. With an accuracy of 99.8%, this model significantly outperforms others that utilize different similarity measures of drugs and target proteins. In addition, the validation of the DTI predictions of this model demonstrates the ability of our method to discover missing potential DTIs.https://peerj.com/articles/cs-1124.pdfHeterogeneous networkNetwork propagationSimilarity measuresDrug-target associationsDrug repurposingForward selection algorithm |
| spellingShingle | Piyanut Tangmanussukum Thitipong Kawichai Apichat Suratanee Kitiporn Plaimas Heterogeneous network propagation with forward similarity integration to enhance drug–target association prediction PeerJ Computer Science Heterogeneous network Network propagation Similarity measures Drug-target associations Drug repurposing Forward selection algorithm |
| title | Heterogeneous network propagation with forward similarity integration to enhance drug–target association prediction |
| title_full | Heterogeneous network propagation with forward similarity integration to enhance drug–target association prediction |
| title_fullStr | Heterogeneous network propagation with forward similarity integration to enhance drug–target association prediction |
| title_full_unstemmed | Heterogeneous network propagation with forward similarity integration to enhance drug–target association prediction |
| title_short | Heterogeneous network propagation with forward similarity integration to enhance drug–target association prediction |
| title_sort | heterogeneous network propagation with forward similarity integration to enhance drug target association prediction |
| topic | Heterogeneous network Network propagation Similarity measures Drug-target associations Drug repurposing Forward selection algorithm |
| url | https://peerj.com/articles/cs-1124.pdf |
| work_keys_str_mv | AT piyanuttangmanussukum heterogeneousnetworkpropagationwithforwardsimilarityintegrationtoenhancedrugtargetassociationprediction AT thitipongkawichai heterogeneousnetworkpropagationwithforwardsimilarityintegrationtoenhancedrugtargetassociationprediction AT apichatsuratanee heterogeneousnetworkpropagationwithforwardsimilarityintegrationtoenhancedrugtargetassociationprediction AT kitipornplaimas heterogeneousnetworkpropagationwithforwardsimilarityintegrationtoenhancedrugtargetassociationprediction |