EMUDRA: Ensemble of Multiple Drug Repositioning Approaches to improve prediction accuracy
Open accessAbstract
Motivation: Availability of large-scale genomic, epigenetic and proteomic data in complex diseases makes it possible to objectively and comprehensively identify the therapeutic targets that can lead to new therapies. The Connectivity Map has been widely used to explore novel indications of existing drugs. However, the prediction accuracy of the existing methods, such as Kolmogorov–Smirnov statistic remains low. Here we present a novel high-performance drug repositioning approach that improves over the state-of-the-art methods. Results: We first designed an expression weighted cosine (EWCos) method to minimize the influence of the uninformative expression changes and then developed an ensemble approach termed ensemble of multiple drug repositioning approaches (EMUDRA) to integrate EWCos and three existing state-of-the-art methods. EMUDRA significantly outperformed individual drug repositioning methods when applied to simulated and independent evaluation datasets. We predicted using EMUDRA and experimentally validated an antibiotic rifabutin as an inhibitor of cell growth in triple negative breast cancer. EMUDRA can identify drugs that more effectively target disease gene signatures and will thus be a useful tool for identifying novel therapies for complex diseases and predicting new indications for existing drugs. Availability and implementation: The EMUDRA R package is available at doi: 10.7303/syn11510888. Supplementary information: Supplementary data are available at Bioinformatics online.
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A particular candidate compound identified using molecular signature data alone using an in silico drug repositioning algorithm was able to increase the expression level of ATP6V1A and restore long-term potentiation and memory formation in both cultured neurons and AD mouse models.
“For instance, a particular candidate compound identified using molecular signature data alone using an in silico drug repositioning algorithm (47) was able to increase the expression level of ATP6V1A and restore long-term potentiation and memory formation in both cultured neurons and AD mouse models (19).”
From Molecular subtyping of Alzheimer’s disease using RNA sequencing data reveals novel mechanisms and targets (2021) · cites this paper as [47]
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“For instance, a particular candidate compound identified using molecular signature data alone using an in silico drug repositioning algorithm (47) was able to increase the expression level of ATP6V1A and restore long-term potentiation and memory formation in both cultured neurons and AD mouse models (19).”
BackgroundSelf-citation
- Authors
- Zhou, X., Wang, M., Katsyv, I., Irie, H., & Zhang, B.
- Affiliations
- Icahn School of Medicine at Mount Sinai (US)
- Tisch Cancer Institute (US)
- Corresponding author
- Zhang, Bin
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-04-24
- Volume
- 34
- Issue
- 18
- Pages
- 3151-3159
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC6138000
- OpenAlex
- W2799482038
- Cited by
- 43
- References
- 52
- Citations by year
- 2025: 7 · 2024: 6 · 2023: 3 · 2022: 6 · 2021: 10 · 2020: 6 · 2019: 3 · 2018: 1
- FWCI
- 3.73
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Bioinformatics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (R01AG046170, RF1AG054014, U01AG052411, RF1AG057440, R01AG057907, R01CA163772, U01AI111598)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- drug repurposing, Connectivity Map, ensemble learning, Kolmogorov-Smirnov statistic, drug target prediction, triple-negative breast cancer, rifabutin, drug target identification
- MeSH terms
- Algorithms, Genomics, Proteomics, Epigenomics, Drug Repositioning
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