期刊
CURRENT PHARMACEUTICAL DESIGN
卷 25, 期 43, 页码 4589-4599出版社
BENTHAM SCIENCE PUBL LTD
DOI: 10.2174/1381612825666191210160906
关键词
Amyloid; alzheimer disease; gene expression; single-cell analysis; drug discovery; cell line
资金
- KAKENHI [19H05270, 17K00417]
- Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arab [KEP-8-611-38]
- DSR
- Grants-in-Aid for Scientific Research [17K00417, 19H05270] Funding Source: KAKEN
Background: Identifying effective candidate drug compounds in patients with neurological disorders based on gene expression data is of great importance to the neurology field. By identifying effective candidate drugs to a given neurological disorder, neurologists would (1) reduce the time searching for effective treatments; and (2) gain additional useful information that leads to a better treatment outcome. Although there are many strategies to screen drug candidate in pre-clinical stage, it is not easy to check if candidate drug compounds can also be effective to human. Objective: We tried to propose a strategy to screen genes whose expression is altered in model animal experiments to be compared with gene expressed differentially with drug treatment to human cell lines. Methods: Recently proposed tensor decomposition (TD) based unsupervised feature extraction (FE) is applied to single cell (sc) RNA-seq experiments of Alzheimer's disease model animal mouse brain. Results: Four hundreds and one genes are screened as those differentially expressed during A beta accumulation as age progresses. These genes are significantly overlapped with those expressed differentially with the known drug treatments for three independent data sets: LINCS, DrugMatrix, and GEO. Conclusion: Our strategy, application of TD based unsupervised FE, is useful one to screen drug candidate compounds using scRNA-seq data set.
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