MOKPE : drug-target interaction prediction via manifold optimization based kernel preserving embedding
© 2023. The Author(s)..
BACKGROUND: In many applications of bioinformatics, data stem from distinct heterogeneous sources. One of the well-known examples is the identification of drug-target interactions (DTIs), which is of significant importance in drug discovery. In this paper, we propose a novel framework, manifold optimization based kernel preserving embedding (MOKPE), to efficiently solve the problem of modeling heterogeneous data. Our model projects heterogeneous drug and target data into a unified embedding space by preserving drug-target interactions and drug-drug, target-target similarities simultaneously.
RESULTS: We performed ten replications of ten-fold cross validation on four different drug-target interaction network data sets for predicting DTIs for previously unseen drugs. The classification evaluation metrics showed better or comparable performance compared to previous similarity-based state-of-the-art methods. We also evaluated MOKPE on predicting unknown DTIs of a given network. Our implementation of the proposed algorithm in R together with the scripts that replicate the reported experiments is publicly available at https://github.com/ocbinatli/mokpe.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:24 |
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| Enthalten in: |
BMC bioinformatics - 24(2023), 1 vom: 05. Juli, Seite 276 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Binatlı, Oğuz C [VerfasserIn] |
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| Links: |
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| Themen: |
Drug–target interaction prediction |
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| Anmerkungen: |
Date Completed 07.07.2023 Date Revised 18.07.2023 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1186/s12859-023-05401-1 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM359090737 |
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| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023. The Author(s). | ||
| 520 | |a BACKGROUND: In many applications of bioinformatics, data stem from distinct heterogeneous sources. One of the well-known examples is the identification of drug-target interactions (DTIs), which is of significant importance in drug discovery. In this paper, we propose a novel framework, manifold optimization based kernel preserving embedding (MOKPE), to efficiently solve the problem of modeling heterogeneous data. Our model projects heterogeneous drug and target data into a unified embedding space by preserving drug-target interactions and drug-drug, target-target similarities simultaneously | ||
| 520 | |a RESULTS: We performed ten replications of ten-fold cross validation on four different drug-target interaction network data sets for predicting DTIs for previously unseen drugs. The classification evaluation metrics showed better or comparable performance compared to previous similarity-based state-of-the-art methods. We also evaluated MOKPE on predicting unknown DTIs of a given network. Our implementation of the proposed algorithm in R together with the scripts that replicate the reported experiments is publicly available at https://github.com/ocbinatli/mokpe | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Drug repurposing | |
| 650 | 4 | |a Drug–target interaction prediction | |
| 650 | 4 | |a Kernel methods | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Manifold optimization | |
| 700 | 1 | |a Gönen, Mehmet |e verfasserin |4 aut | |
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