A benchmark-driven approach to reconstruct metabolic networks for studying cancer metabolism
Fig 9
Normalized growth prediction of GEMs generated using noisy expression data.
(A) CORDA, (B) FASTCORMICS, (C) GIMME, (D) PRIME, (E) TRFBA, and (F) iMAT. Only GEMs capable of predicting growth are shown. The x-axis shows the spearman correlation coefficient between each set of noisy data and original expression profile ranging from 1 (original) to R < 0.004 (random). For a better comparison, growth rates were normalized to the maximum value.