Using multiple drug similarity networks to promote adverse drug event detection
The occurrence of an adverse drug event (ADE) has become a serious social concern of public health. Early detection of ADEs can lower the risk of drug safety as well as the expense of the drug. While post-market spontaneous reports of ADEs remain a cornerstone of pharmacovigilance, most existing sig...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-11-01
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| Series: | Heliyon |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024157593 |
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| author | Biswajit Padhi Ruoqi Liu Yuedi Yang Xueqiao Peng Lang Li Pengyue Zhang Ping Zhang |
| author_facet | Biswajit Padhi Ruoqi Liu Yuedi Yang Xueqiao Peng Lang Li Pengyue Zhang Ping Zhang |
| author_sort | Biswajit Padhi |
| collection | DOAJ |
| description | The occurrence of an adverse drug event (ADE) has become a serious social concern of public health. Early detection of ADEs can lower the risk of drug safety as well as the expense of the drug. While post-market spontaneous reports of ADEs remain a cornerstone of pharmacovigilance, most existing signal detection algorithms rely on substantial accumulated data, limiting their applicability to early ADE detection when reports are scarce. To address this issue, we propose a label propagation model for generating enhanced drug safety signals using multiple drug features. We first construct multiple drug similarity networks using a range of drug features. We then calculate initial drug safety signals using conventional signal detection algorithms. These original signals are subsequently propagated across each drug similarity network to obtain enhanced drug safety signals. We evaluate our proposed model using two common signal detection algorithms on data from the FDA Adverse Event Reporting System (FAERS). Results demonstrate that enhanced drug safety signals with pre-clinical information outperform the standard safety signal detection algorithms on early ADE detection. In addition, we systematically evaluate the performance of different drug similarities against different types of ADEs. Furthermore, we have developed a web interface (http://drug-drug-sim.aimedlab.net/) to display our multiple drug similarity scores, facilitating access to this valuable resource for drug safety monitoring. |
| format | Article |
| id | doaj-art-0bca9f27853a4c4ab1648a86cc6fe3b3 |
| institution | DOAJ |
| issn | 2405-8440 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-0bca9f27853a4c4ab1648a86cc6fe3b32025-08-20T02:48:58ZengElsevierHeliyon2405-84402024-11-011022e3972810.1016/j.heliyon.2024.e39728Using multiple drug similarity networks to promote adverse drug event detectionBiswajit Padhi0Ruoqi Liu1Yuedi Yang2Xueqiao Peng3Lang Li4Pengyue Zhang5Ping Zhang6Department of Computer Science and Engineering, The Ohio State University, 2015 Neil Ave, Columbus, OH 43210, USADepartment of Computer Science and Engineering, The Ohio State University, 2015 Neil Ave, Columbus, OH 43210, USADepartment of Biostatistics and Health Data Science, Indiana University School of Medicine, 410 W. 10th Street HITS 3000, Indianapolis, IN 46202, USADepartment of Computer Science and Engineering, The Ohio State University, 2015 Neil Ave, Columbus, OH 43210, USADepartment of Biomedical Informatics, The Ohio State University, 1800 Cannon Drive, Columbus, OH 43210, USADepartment of Biostatistics and Health Data Science, Indiana University School of Medicine, 410 W. 10th Street HITS 3000, Indianapolis, IN 46202, USA; Corresponding author.Department of Computer Science and Engineering, The Ohio State University, 2015 Neil Ave, Columbus, OH 43210, USA; Department of Biomedical Informatics, The Ohio State University, 1800 Cannon Drive, Columbus, OH 43210, USA; Translational Data Analytics institute, The Ohio State University, 1760 Neil Ave, Columbus, OH 43210, USA; Corresponding author at: Department of Computer Science and Engineering, The Ohio State University, 2015 Neil Ave, Columbus, OH 43210, USA.The occurrence of an adverse drug event (ADE) has become a serious social concern of public health. Early detection of ADEs can lower the risk of drug safety as well as the expense of the drug. While post-market spontaneous reports of ADEs remain a cornerstone of pharmacovigilance, most existing signal detection algorithms rely on substantial accumulated data, limiting their applicability to early ADE detection when reports are scarce. To address this issue, we propose a label propagation model for generating enhanced drug safety signals using multiple drug features. We first construct multiple drug similarity networks using a range of drug features. We then calculate initial drug safety signals using conventional signal detection algorithms. These original signals are subsequently propagated across each drug similarity network to obtain enhanced drug safety signals. We evaluate our proposed model using two common signal detection algorithms on data from the FDA Adverse Event Reporting System (FAERS). Results demonstrate that enhanced drug safety signals with pre-clinical information outperform the standard safety signal detection algorithms on early ADE detection. In addition, we systematically evaluate the performance of different drug similarities against different types of ADEs. Furthermore, we have developed a web interface (http://drug-drug-sim.aimedlab.net/) to display our multiple drug similarity scores, facilitating access to this valuable resource for drug safety monitoring.http://www.sciencedirect.com/science/article/pii/S2405844024157593 |
| spellingShingle | Biswajit Padhi Ruoqi Liu Yuedi Yang Xueqiao Peng Lang Li Pengyue Zhang Ping Zhang Using multiple drug similarity networks to promote adverse drug event detection Heliyon |
| title | Using multiple drug similarity networks to promote adverse drug event detection |
| title_full | Using multiple drug similarity networks to promote adverse drug event detection |
| title_fullStr | Using multiple drug similarity networks to promote adverse drug event detection |
| title_full_unstemmed | Using multiple drug similarity networks to promote adverse drug event detection |
| title_short | Using multiple drug similarity networks to promote adverse drug event detection |
| title_sort | using multiple drug similarity networks to promote adverse drug event detection |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024157593 |
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