Fig. 1: FRoGS can extract weak pathway signals.
From: Drug target prediction through deep learning functional representation of gene signatures
a Comparison between two hypothetical gene signatures. Only gene A and B in Pathway W are considered overlapped based on gene identity (top), similar to the use of one-hot encoding in NLP. Genes A-F contribute to signature overlap if all genes of the same functions W are considered (bottom), similar to the use of word2vec. b t-SNE projection of gene embedding vectors, where each marker represents a gene. Markers are colored by their top-level functions annotated in GO. c Each of the 460 Reactome41 pathways was used to simulate foreground gene signatures generated under varying signal levels with λ at 5, 10, 15, and 20. The separation between foreground-foreground and foreground-background pairs is defined as -log10(p) based on the one-sided Wilcoxon signed-rank test (n = 200 simulations). The larger the value, the more sensitive the method can separate the two types of signature pairs. Each pathway contributes to one data point in each box plot. Box-and-whisker plots show the median (center line), 25th, and 75th percentile (lower and upper boundary), with 1.5 × inter-quartile range indicated by whiskers and outliers shown as individual data points. Source data are provided as a Source Data file.
