Fig. 7
From: Deep learning enables the quantification of browning capacity of human adipose samples
Development and validation of absolute human browning capacity index. A Pipeline for the development of absolute HBI (absHBI) using machine learning. B Correlations between the rankings of DHRS11, REEP6 and STX11 and F72 (n = 663). C Normalized relative expression rankings (RERs) of signature genes (n = 663). Samples are sorted by F72 levels from low to high. D The absHBI levels of white and brown-like fat samples (n = 59). WAT_pertb: WAT samples treated with known small molecules. E Volcano plot showing the Pearson coefficients between absHBI and genome-wide gene expression levels in IR group. F GO enrichment analysis to the genes that positively correlated to absHBI levels in IR patients