Drug Repositioning Based on Deep Sparse Autoencoder and Drug–Disease Similarity

Lei, Song; Lei, Xiujuan; Chen, Ming; Pan, Yi

doi:10.1007/s12539-023-00593-9

Cited by 4 publications

(1 citation statement)

References 56 publications

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“…Initial research treated DTI prediction as a binary classification task, solely distinguishing between combination and non-combination categories. Inspired by the methods employed in other association prediction studies in the field of bioinformatics [3][4][5][6][7][8], these methods incorporated pharmacological data of drugs and targets or constructed heterogeneous networks that linked drugs, targets, and other biological entities for prediction [9]. However, these methods incur some degree of information loss, as well as challenges including determining the threshold for combination and non-combination, and the lack of reliable non-combination samples [10].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Drug-Target Affinity Using Attention Neural Network

Tang,

Lei,

Zhang

2024

IJMS

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Studying drug-target interactions (DTIs) is the foundational and crucial phase in drug discovery. Biochemical experiments, while being the most reliable method for determining drug-target affinity (DTA), are time-consuming and costly, making it challenging to meet the current demands for swift and efficient drug development. Consequently, computational DTA prediction methods have emerged as indispensable tools for this research. In this article, we propose a novel deep learning algorithm named GRA-DTA, for DTA prediction. Specifically, we introduce Bidirectional Gated Recurrent Unit (BiGRU) combined with a soft attention mechanism to learn target representations. We employ Graph Sample and Aggregate (GraphSAGE) to learn drug representation, especially to distinguish the different features of drug and target representations and their dimensional contributions. We merge drug and target representations by an attention neural network (ANN) to learn drug-target pair representations, which are fed into fully connected layers to yield predictive DTA. The experimental results showed that GRA-DTA achieved mean squared error of 0.142 and 0.225 and concordance index reached 0.897 and 0.890 on the benchmark datasets KIBA and Davis, respectively, surpassing the most state-of-the-art DTA prediction algorithms.

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Section: Introductionmentioning

confidence: 99%

Prediction of Drug-Target Affinity Using Attention Neural Network

Tang,

Lei,

Zhang

2024

IJMS

Self Cite

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DRTerHGAT: A drug repurposing method based on the ternary heterogeneous graph attention network

He,

Xie,

Huang

et al. 2024

Journal of Molecular Graphics and Modelling

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TIWMFLP: Two-Tier Interactive Weighted Matrix Factorization and Label Propagation Based on Similarity Matrix Fusion for Drug-Disease Association Prediction

Liu,

Wang,

Zhang

et al. 2024

J. Chem. Inf. Model.

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Accurately identifying new therapeutic uses for drugs is crucial for advancing pharmaceutical research and development. Matrix factorization is often used in association prediction due to its simplicity and high interpretability. However, existing matrix factorization models do not enable real-time interaction between molecular feature matrices and similarity matrices, nor do they consider the geometric structure of the matrices. Additionally, efficiently integrating multisource data remains a significant challenge. To address these issues, we propose a two-tier interactive weighted matrix factorization and label propagation model based on similarity matrix fusion (TIWMFLP) to assist in personalized treatment. First, we calculate the Gaussian and Laplace kernel similarities for drugs and diseases using known drug-disease associations. We then introduce a new multisource similarity fusion method, called similarity matrix fusion (SMF), to integrate these drug/disease similarities. SMF not only considers the different contributions represented by each neighbor but also incorporates drug-disease association information to enhance the contextual topological relationships and potential features of each drug/disease node in the network. Second, we innovatively developed a twotier interactive weighted matrix factorization (TIWMF) method to process three biological networks. This method realizes for the first time the real-time interaction between the drug/disease feature matrix and its similarity matrix, allowing for a better capture of the complex relationships between drugs and diseases. Additionally, the weighted matrix of the drug/disease similarity matrix is introduced to preserve the underlying structure of the similarity matrix. Finally, the label propagation algorithm makes predictions based on the three updated biological networks. Experimental outcomes reveal that TIWMFLP consistently surpasses state-of-the-art models on four drug-disease data sets, two small molecule-miRNA data sets, and one miRNA-disease data set.

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Drug Repositioning Based on Deep Sparse Autoencoder and Drug–Disease Similarity

Cited by 4 publications

References 56 publications

Prediction of Drug-Target Affinity Using Attention Neural Network

Prediction of Drug-Target Affinity Using Attention Neural Network

DRTerHGAT: A drug repurposing method based on the ternary heterogeneous graph attention network

TIWMFLP: Two-Tier Interactive Weighted Matrix Factorization and Label Propagation Based on Similarity Matrix Fusion for Drug-Disease Association Prediction

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