A novel individualized drug repositioning approach for predicting personalized candidate drugs for type 1 diabetes mellitus
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Hong Zheng
Abstract
The existence of high cost-consuming and high rate of drug failures suggests the promotion of drug repositioning in drug discovery. Existing drug repositioning techniques mainly focus on discovering candidate drugs for a kind of disease, and are not suitable for predicting candidate drugs for an individual sample. Type 1 diabetes mellitus (T1DM) is a disorder of glucose homeostasis caused by autoimmune destruction of the pancreatic β-cell. Here, we present a novel single sample drug repositioning approach for predicting personalized candidate drugs for T1DM. Our method is based on the observation of drug-disease associations by measuring the similarities of individualized pathway aberrance induced by disease and various drugs using a Kolmogorov-Smirnov weighted Enrichment Score algorithm. Using this method, we predicted several underlying candidate drugs for T1DM. Some of them have been reported for the treatment of diabetes mellitus, and some with a current indication to treat other diseases might be repurposed to treat T1DM. This study conducts drug discovery via detecting the functional connections among disease and drug action, on a personalized or customized basis. Our framework provides a rational way for systematic personalized drug discovery of complex diseases and contributes to the future application of custom therapeutic decisions.
Conflict of interest statement: The authors declare that there is no conflict of interest that could be perceived as prejudicial to the impartiality of the reported research.
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Artikel in diesem Heft
- Research Articles
- Stability selection for lasso, ridge and elastic net implemented with AFT models
- A novel individualized drug repositioning approach for predicting personalized candidate drugs for type 1 diabetes mellitus
- Clustering methods for single-cell RNA-sequencing expression data: performance evaluation with varying sample sizes and cell compositions
- Bi-level feature selection in high dimensional AFT models with applications to a genomic study
Artikel in diesem Heft
- Research Articles
- Stability selection for lasso, ridge and elastic net implemented with AFT models
- A novel individualized drug repositioning approach for predicting personalized candidate drugs for type 1 diabetes mellitus
- Clustering methods for single-cell RNA-sequencing expression data: performance evaluation with varying sample sizes and cell compositions
- Bi-level feature selection in high dimensional AFT models with applications to a genomic study
