ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2014
Towards drug repositioning: a unified computational framework for integrating multiple aspects of drug similarity and disease similarity.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
30 citing papers in PubMed, 71 citations in OpenAlex.
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- A subspace learning aided matrix factorization for drug repurposing.BioImpacts : BI · 2025Article
- Computing Drug-Drug Similarity from Patient-Centric Data.Bioengineering (Basel, Switzerland) · 2023Article
- Artificial intelligence-based decision support model for new drug development planning.Expert systems with applications · 2022Article
- Computationally repurposing drugs for breast cancer subtypes using a network-based approach.BMC bioinformatics · 2022Article
- Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Topological network measures for drug repositioning.Briefings in bioinformatics · 2021Article
- MGRL: Predicting Drug-Disease Associations Based on Multi-Graph Representation Learning.Frontiers in genetics · 2021Article
- PIMD: An Integrative Approach for Drug Repositioning Using Multiple Characterization Fusion.Genomics, proteomics & bioinformatics · 2020Article
- A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions.Journal of cheminformatics · 2020Review
- iDrug: Integration of drug repositioning and drug-target prediction via cross-network embedding.PLoS computational biology · 2020Article
- The assessment of efficient representation of drug features using deep learning for drug repositioning.BMC bioinformatics · 2019Article
- Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities.An international journal on information fusion · 2019Article
- Additional Neural Matrix Factorization model for computational drug repositioning.BMC bioinformatics · 2019Article
- AI in Health: State of the Art, Challenges, and Future Directions.Yearbook of medical informatics · 2019Review
- Inferring Drug-Related Diseases Based on Convolutional Neural Network and Gated Recurrent Unit.Molecules (Basel, Switzerland) · 2019Article
- A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition.Genomics & informatics · 2019Article
- The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019Article
- Non-Negative Matrix Factorization for Drug Repositioning: Experiments with the repoDB Dataset.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
- Computational drug repositioning using meta-path-based semantic network analysis.BMC systems biology · 2018Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In response to the high cost and high risk associated with traditional de novo drug discovery, investigation of potential additional uses for existing drugs, also known as drug repositioning, has attracted increasing attention from both the pharmaceutical industry and the research community. In this paper, we propose a unified computational framework, called DDR, to predict novel drug-disease associations. DDR formulates the task of hypothesis generation for drug repositioning as a constrained nonlinear optimization problem. It utilizes multiple drug similarity networks, multiple disease similarity networks, and known drug-disease associations to explore potential new associations among drugs and diseases with no known links. A large-scale study was conducted using 799 drugs against 719 diseases. Experimental results demonstrated the effectiveness of the approach. In addition, DDR ranked drug and disease information sources based on their contributions to the prediction, thus paving the way for prioritizing multiple data sources and building more reliable drug repositioning models. Particularly, some of our novel predictions of drug-disease associations were supported by clinical trials databases, showing that DDR could serve as a useful tool in drug discovery to efficiently identify potential novel uses for existing drugs.
Indexed as
Identifiers
25954437PMC4419869W2402674527What OpenQuestion holds
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.