MOKPE: drug–target interaction prediction via manifold optimization based kernel preserving embedding

MOKPE: drug–target interaction prediction via manifold optimization based kernel preserving embedding

Abstract 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, manif...

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Bibliographic Details
Main Authors: Oğuz C. Binatlı, Mehmet Gönen
Format: Article
Language:English
Published: BMC 2023-07-01
Series:BMC Bioinformatics
Subjects:
Drug–target interaction prediction
Drug repurposing
Manifold optimization
Kernel methods
Machine learning
Online Access:https://doi.org/10.1186/s12859-023-05401-1

Internet

https://doi.org/10.1186/s12859-023-05401-1

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