Fig. 2: Collection and evaluation of drug-associated gene lists.
From: Machine learning identifies candidates for drug repurposing in Alzheimer’s disease

a Overview of the 3′ DGE experimental protocol used to derive drug-associated gene expression signatures. ReNcell VM human neural progenitor cells were plated and differentiated for 10 days, resulting in a mixed cell population of neurons, glia, and oligodendrocytes. The mixed culture was subsequently treated with a panel of drugs (Supplementary Data 3) at 10 µM for 24 h and frozen in a lysis buffer until library preparation. RNA was extracted and reverse transcribed into cDNA in each well of the plate, followed by pooling and preparation of mRNA libraries. After sequencing, mRNA reads were demultiplexed according to well barcodes, and the resulting gene expression profiles were processed by a standard differential expression method to derive drug-associated gene lists. b A highlight of two compounds whose gene lists consistently yield improved performance over the randomly selected lists of equal length. Shown is performance associated with predicting early-vs-late disease stages in several AMP-AD datasets. Each row corresponds to an evaluation of gene lists in a single dataset; MSBB evaluation is subdivided into four brain regions, specified as Brodmann Area. The vertical line denotes performance of the drug-associated list, while the background distribution shows performance of gene lists randomly selected from the same dataset. The drugs are annotated with their nominal targets. The unadjusted p-values were computed with a one-sided empirical test, by counting the fraction of randomly selected lists that outperformed the corresponding drug-associated lists.