In Bioinformatics (Oxford, England)
MOTIVATION : Many methods have been proposed for mapping the targets of TFs from gene expression data. It is known that combining outputs from multiple methods can improve performance. To date, outputs have been combined by using either simplistic formulae, such as geometric mean, or carefully hand-tuned formulae that may not generalize well to new inputs. Finally, evaluation of accuracy has been challenging due to the lack of genome-scale, ground-truth networks.
RESULTS : We developed NetProphet3, which combines scores from multiple analyses automatically, using a tree boosting algorithm trained on TF binding location data. We also developed three independent, genome-scale evaluation metrics. By these metrics, NetProphet3 is more accurate than other commonly used packages, including NetProphet2, when gene expression data from direct TF perturbations are available. Furthermore, its integration mode can forge a consensus network from gene expression data and TF binding location data.
AVAILABILITY : Open source: https://zenodo.org/record/7504131#.Y7Wu3i-B2x8.
SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.
Abid Dhoha, Brent Michael R
2023-Jan-24
