ArticleScientific reports2017
Screening drug-target interactions with positive-unlabeled learning.
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 15 papers.
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Who cites it
15 citing papers in PubMed.
- ProDTI: prior-guided positive-unlabeled learning for robust drug-target interaction prediction.Bioinformatics (Oxford, England) · 2026Article
- KGTC-DDA:Drug-disease association prediction based on parallel graph isomorphism and transformer networks with cross-attention.Biochemistry and biophysics reports · 2026Article
- Improving drug repositioning with negative data labeling using large language models.Journal of cheminformatics · 2025Article
- An Enhanced Computational Approach Using Multi-kernel Positive Unlabeled Learning for Predicting Drug-target Interactions.Current computer-aided drug design · 2025Article
- Predicting protein functions using positive-unlabeled ranking with ontology-based priors.Bioinformatics (Oxford, England) · 2024Article
- Benchmarking compound activity prediction for real-world drug discovery applications.Communications chemistry · 2024Article
- A Robust Drug-Target Interaction Prediction Framework with Capsule Network and Transfer Learning.International journal of molecular sciences · 2023Article
- Validation approaches for computational drug repurposing: a review.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Review
- Drug-Target Interaction Prediction Based on Multisource Information Weighted Fusion.Contrast media & molecular imaging · 2021Article
- Identification of miRNA-Mediated Subpathways as Prostate Cancer Biomarkers Based on Topological Inference in a Machine Learning Process Using Integrated Gene and miRNA Expression Data.Frontiers in genetics · 2021Article
- Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe-Disease Association Prediction.Frontiers in microbiology · 2021Article
- Comprehensive circRNA Expression Profile and Construction of circRNAs-Related ceRNA Network in a Mouse Model of Autism.Frontiers in genetics · 2020Article
- Identifying Effective Antiviral Drugs Against SARS-CoV-2 by Drug Repositioning Through Virus-Drug Association Prediction.Frontiers in genetics · 2020Article
- Revealing Drug-Target Interactions with Computational Models and Algorithms.Molecules (Basel, Switzerland) · 2019Review
- An integrated framework for the identification of potential miRNA-disease association based on novel negative samples extraction strategy.RNA biology · 2019Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying drug-target interaction (DTI) candidates is crucial for drug repositioning. However, usually only positive DTIs are deposited in known databases, which challenges computational methods to predict novel DTIs due to the lack of negative samples. To overcome this dilemma, researchers usually randomly select negative samples from unlabeled drug-target pairs, which introduces a lot of false-positives. In this study, a negative sample extraction method named NDTISE is first developed to screen strong negative DTI examples based on positive-unlabeled learning. A novel DTI screening framework, PUDTI, is then designed to infer new drug repositioning candidates by integrating NDTISE, probabilities that remaining ambiguous samples belong to the positive and negative classes, and an SVM-based optimization model. We investigated the effectiveness of NDTISE on a DTI data provided by NCPIS. NDTISE is much better than random selection and slightly outperforms NCPIS. We then compared PUDTI with 6 state-of-the-art methods on 4 classes of DTI datasets from human enzymes, ion channels, GPCRs and nuclear receptors. PUDTI achieved the highest AUC among the 7 methods on all 4 datasets. Finally, we validated a few top predicted DTIs through mining independent drug databases and literatures. In conclusion, PUDTI provides an effective pre-filtering method for new drug design.
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