Neurological disorder drug discovery from gene expression with tensor decomposition

BackgroundIdentifying 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 be also effective to human.\n\nObjectiveWe tried to propose a strategy to screen genes whose expression is altered in model animal experiments to be compared with gene expressed differentically with drug treatment to human cell lines.\n\nMethodsRecently proposed tensor decomposition (TD) based unsupervised feature extraction (FE) is applied to single cell (sc) RNA-seq experiments of Alzheimers disease model animal mouse brain.\n\nResultsFour 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.\n\nConclusionOur strategy, application of TD based unsupervised FE, is useful one to screen drug candidate compounds using scRNA-seq data set.