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- W4387440466 abstract "Abstract Motivation Numerous high-accuracy drug-target affinity (DTA) prediction models, whose performance is heavily reliant on the drug and target feature information, are developed at the expense of complexity and interpretability. Feature extraction and optimization constitute a critical step that significantly influences the enhancement of model performance, robustness, and interpretability. Many existing studies aim to comprehensively characterize drugs and targets by extracting features from multiple perspectives; However, this approach has drawbacks: i. An abundance of redundant or noisy features. ii. The feature sets often suffer from high dimensionality. Results In this study, to obtain a model with high accuracy and strong interpretability, we utilize various traditional and cutting-edge feature selection and dimensionality reduction techniques to process self-associated features (SAFs) and adjacent associated features (AAFs). These optimized features are then fed into learning to rank (LTR) to achieve efficient DTA prediction. Extensive experimental results on two commonly used datasets indicate that, among various feature optimization methods, the regression tree-based feature selection method is most beneficial for constructing models with good performance and strong robustness. Then, by utilizing Shapley Additive Explanations (SHAP) values and the incremental feature selection (IFS) approach, we obtain that the high-quality feature subset consists of the top 150-dimensional features and the top 20-dimensional features have a breakthrough impact on the DTA prediction. In conclusion, our study thoroughly validates the importance of feature optimization in DTA prediction and serves as inspiration for constructing high-performance and high-interpretable models. Availability https://github.com/RUXIAOQING964914140/FS_DTA. Supplementary information Supplementary data are available at Bioinformatics online." @default.
- W4387440466 created "2023-10-10" @default.
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- W4387440466 date "2023-10-09" @default.
- W4387440466 modified "2023-10-10" @default.
- W4387440466 title "Optimization of drug-target affinity prediction methods through feature processing schemes" @default.
- W4387440466 doi "https://doi.org/10.1093/bioinformatics/btad615" @default.
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- W4387440466 hasPublicationYear "2023" @default.
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