ArticleBMC bioinformatics2019
L
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- The structure-preserving spectral graph neural network for dual kinase inhibitors and synergy scoring in gastric cancer.NPJ digital medicine · 2025Article
- Graph regularized non-negative matrix factorization withBMC bioinformatics · 2023Article
- Graph regularized non-negative matrix factorization with prior knowledge consistency constraint for drug-target interactions prediction.BMC bioinformatics · 2022Article
- Drug-target interaction prediction via multiple classification strategies.BMC bioinformatics · 2022Article
- Drug-target interaction prediction using unifying of graph regularized nuclear norm with bilinear factorization.BMC bioinformatics · 2021Article
- Logistic matrix factorisation and generative adversarial neural network-based method for predicting drug-target interactions.Molecular diversity · 2021Article
- Binding affinity prediction for binary drug-target interactions using semi-supervised transfer learning.Journal of computer-aided molecular design · 2021Article
- DSCMF: prediction of LncRNA-disease associations based on dual sparse collaborative matrix factorization.BMC bioinformatics · 2021Article
- Predicting Protein-Protein Interactions Using Symmetric Logistic Matrix Factorization.Journal of chemical information and modeling · 2021Article
- Coupled matrix-matrix and coupled tensor-matrix completion methods for predicting drug-target interactions.Briefings in bioinformatics · 2021Article
- Drug-Target Interaction Prediction via Dual Laplacian Graph Regularized Logistic Matrix Factorization.BioMed research international · 2021Article
- DTiGEMS+: drug-target interaction prediction using graph embedding, graph mining, and similarity-based techniques.Journal of cheminformatics · 2020Article
- RCMF: a robust collaborative matrix factorization method to predict miRNA-disease associations.BMC bioinformatics · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPredicting drug-target interactions is time-consuming and expensive. It is important to present the accuracy of the calculation method. There are many algorithms to predict global interactions, some of which use drug-target networks for prediction (ie, a bipartite graph of bound drug pairs and targets known to interact). Although these algorithms can predict some drug-target interactions to some extent, there is little effect for some new drugs or targets that have no known interaction.
resultsSince the datasets are usually located at or near low-dimensional nonlinear manifolds, we propose an improved GRMF (graph regularized matrix factorization) method to learn these flow patterns in combination with the previous matrix-decomposition method. In addition, we use one of the pre-processing steps previously proposed to improve the accuracy of the prediction.
conclusionsCross-validation is used to evaluate our method, and simulation experiments are used to predict new interactions. In most cases, our method is superior to other methods. Finally, some examples of new drugs and new targets are predicted by performing simulation experiments. And the improved GRMF method can better predict the remaining drug-target interactions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.