ArticleScientific reports2016
Prediction of new drug indications based on clinical data and network modularity.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- MGACL: Prediction Drug-Protein Interaction Based on Meta-Graph Association-Aware Contrastive Learning.Biomolecules · 2024Article
- Joint learning sample similarity and correlation representation for cancer survival prediction.BMC bioinformatics · 2022Article
- A Visual Phenotype-Based Differential Diagnosis Process for Rare Diseases.Interdisciplinary sciences, computational life sciences · 2022Article
- RDmap: a map for exploring rare diseases.Orphanet journal of rare diseases · 2021Article
- Semi-supervised Hierarchical Drug Embedding in Hyperbolic Space.Journal of chemical information and modeling · 2020Article
- The comparison of miRNAs that respond to anti-breast cancer drugs and usnic acid for the treatment of breast cancer.Cytotechnology · 2020Article
- Exploring Drug Treatment Patterns Based on the Action of Drug and Multilayer Network Model.International journal of molecular sciences · 2020Article
- Learning from methylomes: epigenomic correlates of Populus balsamifera traits based on deep learning models of natural DNA methylation.Plant biotechnology journal · 2020Article
- Computational Methods for Identifying Similar Diseases.Molecular therapy. Nucleic acids · 2019Review
- Drug repurposing: a promising tool to accelerate the drug discovery process.Drug discovery today · 2019Review
- Drug prioritization using the semantic properties of a knowledge graph.Scientific reports · 2019Article
- Gradient Boosting Decision Tree-Based Method for Predicting Interactions Between Target Genes and Drugs.Frontiers in genetics · 2019Article
- Predicting Ion Channels Genes and Their Types With Machine Learning Techniques.Frontiers in genetics · 2019Article
- Application of Machine Learning in Microbiology.Frontiers in microbiology · 2019Review
- A Random Forest Sub-Golgi Protein Classifier Optimized via Dipeptide and Amino Acid Composition Features.Frontiers in bioengineering and biotechnology · 2019Article
- Identification of Drug-Disease Associations Using Information of Molecular Structures and Clinical Symptoms via Deep Convolutional Neural Network.Frontiers in chemistry · 2019Article
- Identification of Phage Viral Proteins With Hybrid Sequence Features.Frontiers in microbiology · 2019Article
- Predicting Potential Drugs for Breast Cancer based on miRNA and Tissue Specificity.International journal of biological sciences · 2018Article
- Drug repurposing from the perspective of pharmaceutical companies.British journal of pharmacology · 2018Review
- Formalizing drug indications on the road to therapeutic intent.Journal of the American Medical Informatics Association : JAMIA · 2017Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug repositioning is commonly done within the drug discovery process in order to adjust or expand the application line of an active molecule. Previous computational methods in this domain mainly focused on shared genes or correlations between genes to construct new drug-disease associations. We propose a method that can not only handle drugs or diseases with or without related genes but consider the network modularity. Our method firstly constructs a drug network and a disease network based on side effects and symptoms respectively. Because similar drugs imply similar diseases, we then cluster the two networks to identify drug and disease modules, and connect all possible drug-disease module pairs. Further, based on known drug-disease associations in CTD and using local connectivity of modules, we predict potential drug-disease associations. Our predictions are validated by testing their overlaps with drug indications reported in published literatures and CTD, and KEGG enrichment analysis are also made on their related genes. The experimental results demonstrate that our approach can complement the current computational approaches and its predictions can provide new clues for the candidate discovery of drug repositioning.
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Registered trials
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.