What is it about?
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 Aβ 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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Why is it important?
We can infer drug with computer, not with experiments.
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This page is a summary of: Neurological Disorder Drug Discovery from Gene Expression with Tensor Decomposition, Current Pharmaceutical Design, January 2020, Bentham Science Publishers, DOI: 10.2174/1381612825666191210160906.
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