ArticleBMC bioinformatics2013
Pathway-based drug repositioning using causal inference.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 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.
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Who cites it
34 citing papers in PubMed, 100 citations in OpenAlex.
- Reconciling multiple connectivity-based systems biology methods for drug repurposing.Briefings in bioinformatics · 2025Review
- Article
- A drug repurposing method based on inhibition effect on gene regulatory network.Computational and structural biotechnology journal · 2023Article
- Knowledge graph analytics platform with LINCS and IDG for Parkinson's disease target illumination.BMC bioinformatics · 2022Article
- Scope of repurposed drugs against the potential targets of the latest variants of SARS-CoV-2.Structural chemistry · 2022Review
- Investigation of pharmacological mechanism of natural product using pathway fingerprints similarity based on "drug-target-pathway" heterogenous network.Journal of cheminformatics · 2021Article
- Application of network link prediction in drug discovery.BMC bioinformatics · 2021Article
- Predicting therapeutic drugs for hepatocellular carcinoma based on tissue-specific pathways.PLoS computational biology · 2021Article
- Repositioned Drugs for Inflammatory Diseases such as Sepsis, Asthma, and Atopic Dermatitis.Biomolecules & therapeutics · 2020Review
- Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities.An international journal on information fusion · 2019Article
- Multimodal network diffusion predicts future disease-gene-chemical associations.Bioinformatics (Oxford, England) · 2019Article
- Translational medicine in the Age of Big Data.Briefings in bioinformatics · 2019Article
- Non-Negative Matrix Factorization for Drug Repositioning: Experiments with the repoDB Dataset.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
- Enabling Web-scale data integration in biomedicine through Linked Open Data.NPJ digital medicine · 2019Review
- Computational systems biology approaches for Parkinson's disease.Cell and tissue research · 2018Review
- Changing Trends in Computational Drug Repositioning.Pharmaceuticals (Basel, Switzerland) · 2018Review
- DR2DI: a powerful computational tool for predicting novel drug-disease associations.Journal of computer-aided molecular design · 2018Article
- Identifying heterogeneous subtypes of gastric cancer and subtype‑specific subpaths of microRNA‑target pathways.Molecular medicine reports · 2018Article
- DeCoST: A New Approach in Drug Repurposing From Control System Theory.Frontiers in pharmacology · 2018Article
- PhLeGrA: Graph Analytics in Pharmacology over the Web of Life Sciences Linked Open Data.Proceedings of the ... International World-Wide Web Conference. International WWW Conference · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundRecent in vivo studies showed new hopes of drug repositioning through causality inference from drugs to disease. Inspired by their success, here we present an in silico method for building a causal network (CauseNet) between drugs and diseases, in an attempt to systematically identify new therapeutic uses of existing drugs.
methodsUnlike the traditional 'one drug-one target-one disease' causal model, we simultaneously consider all possible causal chains connecting drugs to diseases via target- and gene-involved pathways based on rich information in several expert-curated knowledge-bases. With statistical learning, our method estimates transition likelihood of each causal chain in the network based on known drug-disease treatment associations (e.g. bexarotene treats skin cancer).
resultsTo demonstrate its validity, our method showed high performance (AUC = 0.859) in cross validation. Moreover, our top scored prediction results are highly enriched in literature and clinical trials. As a showcase of its utility, we show several drugs for potential re-use in Crohn's Disease.
conclusionsWe successfully developed a computational method for discovering new uses of existing drugs based on casual inference in a layered drug-target-pathway-gene- disease network. The results showed that our proposed method enables hypothesis generation from public accessible biological data for 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.