DTI-MHAPR: optimized drug-target interaction prediction via PCA-enhanced features and heterogeneous graph attention networks

DTI-MHAPR: optimized drug-target interaction prediction via PCA-enhanced features and heterogeneous graph attention networks

Abstract Drug-target interactions (DTIs) are pivotal in drug discovery and development, and their accurate identification can significantly expedite the process. Numerous DTI prediction methods have emerged, yet many fail to fully harness the feature information of drugs and targets or address the i...

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Bibliographic Details
Main Authors: Guang Yang, Yinbo Liu, Sijian Wen, Wenxi Chen, Xiaolei Zhu, Yongmei Wang
Format: Article
Language:English
Published: BMC 2025-01-01
Series:BMC Bioinformatics
Subjects:
DTIs prediction
Heterogeneous graph attention networks
Feature concatenation
PCA
Random forest
Online Access:https://doi.org/10.1186/s12859-024-06021-z
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https://doi.org/10.1186/s12859-024-06021-z

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