ArticleScientific reports2017
Predicting drug-target interactions by dual-network integrated logistic matrix factorization.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- Application of Artificial Intelligence In Drug-target Interactions Prediction: A Review.npj biomedical innovations · 2025Review
- DTGHAT: multi-molecule heterogeneous graph transformer based on multi-molecule graph for drug-target identification.Frontiers in pharmacology · 2025Article
- Article
- GCARDTI: Drug-target interaction prediction based on a hybrid mechanism in drug SELFIES.Quantitative biology (Beijing, China) · 2024Article
- NGCN: Drug-target interaction prediction by integrating information and feature learning from heterogeneous network.Journal of cellular and molecular medicine · 2024Article
- BioBLP: a modular framework for learning on multimodal biomedical knowledge graphs.Journal of biomedical semantics · 2023Article
- DRaW: prediction of COVID-19 antivirals by deep learning-an objection on using matrix factorization.BMC bioinformatics · 2023Article
- DeepMPF: deep learning framework for predicting drug-target interactions based on multi-modal representation with meta-path semantic analysis.Journal of translational medicine · 2023Article
- Hyperbolic matrix factorization improves prediction of drug-target associations.Scientific reports · 2023Article
- LM-DTI: a tool of predicting drug-target interactions using the node2vec and network path score methods.Frontiers in genetics · 2023Article
- Prediction of Drug-Target Interaction Using Dual-Network Integrated Logistic Matrix Factorization and Knowledge Graph Embedding.Molecules (Basel, Switzerland) · 2022Article
- Detecting Drug-Target Interactions with Feature Similarity Fusion and Molecular Graphs.Biology · 2022Article
- Novel drug-target interactions via link prediction and network embedding.BMC bioinformatics · 2022Article
- NeuRank: learning to rank with neural networks for drug-target interaction prediction.BMC bioinformatics · 2021Article
- DTi2Vec: Drug-target interaction prediction using network embedding and ensemble learning.Journal of cheminformatics · 2021Article
- Prediction of Drug-Target Interactions by Combining Dual-Tree Complex Wavelet Transform with Ensemble Learning Method.Molecules (Basel, Switzerland) · 2021Article
- Logistic matrix factorisation and generative adversarial neural network-based method for predicting drug-target interactions.Molecular diversity · 2021Article
- Predicting Protein-Protein Interactions Using Symmetric Logistic Matrix Factorization.Journal of chemical information and modeling · 2021Article
- AutoDTI++: deep unsupervised learning for DTI prediction by autoencoders.BMC bioinformatics · 2021Article
- A Machine Learning-Based Biological Drug-Target Interaction Prediction Method for a Tripartite Heterogeneous Network.ACS omega · 2021Article
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3 authors.
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Abstract
In this work, we propose a dual-network integrated logistic matrix factorization (DNILMF) algorithm to predict potential drug-target interactions (DTI). The prediction procedure consists of four steps: (1) inferring new drug/target profiles and constructing profile kernel matrix; (2) diffusing drug profile kernel matrix with drug structure kernel matrix; (3) diffusing target profile kernel matrix with target sequence kernel matrix; and (4) building DNILMF model and smoothing new drug/target predictions based on their neighbors. We compare our algorithm with the state-of-the-art method based on the benchmark dataset. Results indicate that the DNILMF algorithm outperforms the previously reported approaches in terms of AUPR (area under precision-recall curve) and AUC (area under curve of receiver operating characteristic) based on the 5 trials of 10-fold cross-validation. We conclude that the performance improvement depends on not only the proposed objective function, but also the used nonlinear diffusion technique which is important but under studied in the DTI prediction field. In addition, we also compile a new DTI dataset for increasing the diversity of currently available benchmark datasets. The top prediction results for the new dataset are confirmed by experimental studies or supported by other computational research.
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