ArticleMolecules (Basel, Switzerland)2017
Drug-Target Interaction Prediction through Label Propagation with Linear Neighborhood Information.
Article in Molecules (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- IIC-DTI: A Contrastive Learning Enhanced Inter-Intra Molecular Fusing Framework for Drug-Target Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Real-time visualization of drug-target interactions in native subcellular microenvironments for lysosome-targeted drug discovery.Journal of pharmaceutical analysis · 2026Article
- Comprehensive Review on Drug-target Interaction Prediction - Latest Developments and Overview.Current drug discovery technologies · 2024Review
- Small molecule drug and biotech drug interaction prediction based on multi-modal representation learning.BMC bioinformatics · 2022Article
- MSF-UBRW: An Improved Unbalanced Bi-Random Walk Method to Infer Human lncRNA-Disease Associations.Genes · 2022Article
- RoFDT: Identification of Drug-Target Interactions from Protein Sequence and Drug Molecular Structure Using Rotation Forest.Biology · 2022Article
- WLLP: A weighted reconstruction-based linear label propagation algorithm for predicting potential therapeutic agents for COVID-19.Frontiers in microbiology · 2022Article
- NeuRank: learning to rank with neural networks for drug-target interaction prediction.BMC bioinformatics · 2021Article
- Machine learning approaches and databases for prediction of drug-target interaction: a survey paper.Briefings in bioinformatics · 2021Review
- Predicting Drug-Disease Association Based on Ensemble Strategy.Frontiers in genetics · 2021Article
- An Ensemble Learning-Based Method for Inferring Drug-Target Interactions Combining Protein Sequences and Drug Fingerprints.BioMed research international · 2021Article
- Drug-Target Interaction Prediction via Dual Laplacian Graph Regularized Logistic Matrix Factorization.BioMed research international · 2021Article
- Drug-target interaction prediction with tree-ensemble learning and output space reconstruction.BMC bioinformatics · 2020Article
- Predicting Drug-Disease Associations via Multi-Task Learning Based on Collective Matrix Factorization.Frontiers in bioengineering and biotechnology · 2020Article
- A Novel Triple Matrix Factorization Method for Detecting Drug-Side Effect Association Based on Kernel Target Alignment.BioMed research international · 2020Article
- LncRNA-miRNA interaction prediction through sequence-derived linear neighborhood propagation method with information combination.BMC genomics · 2019Article
- Self-Weighted Multi-Kernel Multi-Label Learning for Potential miRNA-Disease Association Prediction.Molecular therapy. Nucleic acids · 2019Article
- Predicting circRNA-Disease Associations Based on circRNA Expression Similarity and Functional Similarity.Frontiers in genetics · 2019Article
- Sequence-based bacterial small RNAs prediction using ensemble learning strategies.BMC bioinformatics · 2018Article
- PWCDA: Path Weighted Method for Predicting circRNA-Disease Associations.International journal of molecular sciences · 2018Article
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3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Interactions between drugs and target proteins provide important information for the drug discovery. Currently, experiments identified only a small number of drug-target interactions. Therefore, the development of computational methods for drug-target interaction prediction is an urgent task of theoretical interest and practical significance. In this paper, we propose a label propagation method with linear neighborhood information (LPLNI) for predicting unobserved drug-target interactions. Firstly, we calculate drug-drug linear neighborhood similarity in the feature spaces, by considering how to reconstruct data points from neighbors. Then, we take similarities as the manifold of drugs, and assume the manifold unchanged in the interaction space. At last, we predict unobserved interactions between known drugs and targets by using drug-drug linear neighborhood similarity and known drug-target interactions. The experiments show that LPLNI can utilize only known drug-target interactions to make high-accuracy predictions on four benchmark datasets. Furthermore, we consider incorporating chemical structures into LPLNI models. Experimental results demonstrate that the model with integrated information (LPLNI-II) can produce improved performances, better than other state-of-the-art methods. The known drug-target interactions are an important information source for computational predictions. The usefulness of the proposed method is demonstrated by cross validation and the case study.
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