ArticleAnalytica chimica acta2016
Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique.
Article in Analytica chimica acta, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Predicting drug-target interactions using matrix factorization with self-paced learning and dual similarity information.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- Random-forest model for drug-target interaction prediction via Kullbeck-Leibler divergence.Journal of cheminformatics · 2022Article
- Prediction of Drug-Target Interaction Using Dual-Network Integrated Logistic Matrix Factorization and Knowledge Graph Embedding.Molecules (Basel, Switzerland) · 2022Article
- GCRNN: graph convolutional recurrent neural network for compound-protein interaction prediction.BMC bioinformatics · 2022Article
- Drug repositioning: A bibliometric analysis.Frontiers in pharmacology · 2022Review
- Discovery of Potential Therapeutic Drugs for COVID-19 Through Logistic Matrix Factorization With Kernel Diffusion.Frontiers in microbiology · 2022Article
- Comprehensive Survey of Recent Drug Discovery Using Deep Learning.International journal of molecular sciences · 2021Review
- Machine learning approaches and databases for prediction of drug-target interaction: a survey paper.Briefings in bioinformatics · 2021Review
- Cascade Deep Forest With Heterogeneous Similarity Measures for Drug-Target Interaction Prediction.Frontiers in genetics · 2021Article
- Drug-target interactions prediction using marginalized denoising model on heterogeneous networks.BMC bioinformatics · 2020Article
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- Open-source chemogenomic data-driven algorithms for predicting drug-target interactions.Briefings in bioinformatics · 2019Review
- A new chemoinformatics approach with improved strategies for effective predictions of potential drugs.Journal of cheminformatics · 2018Article
- iDTI-ESBoost: Identification of Drug Target Interaction Using Evolutionary and Structural Features with Boosting.Scientific reports · 2017Article
- VB-MK-LMF: fusion of drugs, targets and interactions using variational Bayesian multiple kernel logistic matrix factorization.BMC bioinformatics · 2017Article
- Chemistry-based molecular signature underlying the atypia of clozapine.Translational psychiatry · 2017Article
- Predicting drug-target interactions by dual-network integrated logistic matrix factorization.Scientific reports · 2017Article
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3 authors.
Funding
Abstract
Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision-recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as polypharmacology, and may help to accelerate drug discovery by identifying novel drug targets.
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