Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction
Co-administration of two or more drugs simultaneously can result in adverse drug reactions. Identifying drug-drug interactions (DDIs) is necessary, especially for drug development and for repurposing old drugs. DDI prediction can be viewed as a matrix completion task, for which matrix factorization (MF) appears as a suitable solution. This paper presents a novel Graph Regularized Probabilistic Matrix Factorization (GRPMF) method, which incorporates expert knowledge through a novel graph-based regularization strategy within an MF framework. An efficient and sounded optimization algorithm is proposed to solve the resulting non-convex problem in an alternating fashion. The performance of the proposed method is evaluated through the DrugBank dataset, and comparisons are provided against state-of-the-art techniques. The results demonstrate the superior performance of GRPMF when compared to its counterparts.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:27 |
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| Enthalten in: |
IEEE journal of biomedical and health informatics - 27(2023), 5 vom: 16. Mai, Seite 2565-2574 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Jain, Stuti [VerfasserIn] |
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| Links: |
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| Themen: |
Journal Article |
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| Anmerkungen: |
Date Completed 08.05.2023 Date Revised 09.05.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1109/JBHI.2023.3246225 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM35532220X |
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| 520 | |a Co-administration of two or more drugs simultaneously can result in adverse drug reactions. Identifying drug-drug interactions (DDIs) is necessary, especially for drug development and for repurposing old drugs. DDI prediction can be viewed as a matrix completion task, for which matrix factorization (MF) appears as a suitable solution. This paper presents a novel Graph Regularized Probabilistic Matrix Factorization (GRPMF) method, which incorporates expert knowledge through a novel graph-based regularization strategy within an MF framework. An efficient and sounded optimization algorithm is proposed to solve the resulting non-convex problem in an alternating fashion. The performance of the proposed method is evaluated through the DrugBank dataset, and comparisons are provided against state-of-the-art techniques. The results demonstrate the superior performance of GRPMF when compared to its counterparts | ||
| 650 | 4 | |a Journal Article | |
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| 700 | 1 | |a Kumar, Kriti |e verfasserin |4 aut | |
| 700 | 1 | |a Majumdar, Angshul |e verfasserin |4 aut | |
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