RESULTS: We developed NetProphet3, which combines scores from multiple analyses automatically, using a tree boosting algorithm trained on TF binding location data.
Finally, the evaluation of accuracy has been challenging due to the lack of genome-scale, ground-truth networks. 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. It is known that combining outputs from multiple methods can improve performance. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.Ībstract = "MOTIVATION: Many methods have been proposed for mapping the targets of transcription factors (TFs) from gene expression data.
#Machine transcriptions code#
AVAILABILITY AND IMPLEMENTATION: All data and code are available at. Furthermore, its integration mode can forge a consensus network from gene expression data and TF binding location data.
By these metrics, NetProphet3 is more accurate than other commonly used packages, including NetProphet 2.0, when gene expression data from direct TF perturbations are available. We also developed three independent, genome-scale evaluation metrics. MOTIVATION: Many methods have been proposed for mapping the targets of transcription factors (TFs) from gene expression data.
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