ArticleJournal of computer-aided molecular design2018
DR2DI: a powerful computational tool for predicting novel drug-disease associations.
Article in Journal of computer-aided molecular design, 2018. 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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15 citing papers in PubMed, 35 citations in OpenAlex.
- Herb-disease association prediction model based on network consistency projection.Scientific reports · 2025Article
- Fusing graph transformer with multi-aggregate GCN for enhanced drug-disease associations prediction.BMC bioinformatics · 2024Article
- MHCLMDA: multihypergraph contrastive learning for miRNA-disease association prediction.Briefings in bioinformatics · 2023Article
- In-silico computational approaches to study microbiota impacts on diseases and pharmacotherapy.Gut pathogens · 2023Review
- Building a knowledge graph to enable precision medicine.Scientific data · 2023Article
- mintRULS: Prediction of miRNA-mRNA Target Site Interactions Using Regularized Least Square Method.Genes · 2022Article
- MTAGCN: predicting miRNA-target associations in Camellia sinensis var. assamica through graph convolution neural network.BMC bioinformatics · 2022Article
- Drug Repositioning with GraphSAGE and Clustering Constraints Based on Drug and Disease Networks.Frontiers in pharmacology · 2022Article
- Learning from low-rank multimodal representations for predicting disease-drug associations.BMC medical informatics and decision making · 2021Article
- HeTDR: Drug repositioning based on heterogeneous networks and text mining.Patterns (New York, N.Y.) · 2021Article
- Genome-wide discovery of hidden genes mediating known drug-disease association using KDDANet.NPJ genomic medicine · 2021Article
- A Machine Learning-Based Biological Drug-Target Interaction Prediction Method for a Tripartite Heterogeneous Network.ACS omega · 2021Article
- DDIT: An Online Predictor for Multiple Clinical Phenotypic Drug-Disease Associations.Frontiers in pharmacology · 2021Article
- Time-resolved evaluation of compound repositioning predictions on a text-mined knowledge network.BMC bioinformatics · 2019Article
- Predicting Drug-Disease Associations via Using Gaussian Interaction Profile and Kernel-Based Autoencoder.BioMed research international · 2019Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI .
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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 OpenQuestion graph.