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Table 1 Performance of different modeling and bin selection strategies

From: Modeling gene expression using chromatin features in various cellular contexts

  Allbins TSSbin bins.0.2 best5bins bestbin
Simple model 0.772 (2.77) 0.836 (2.40) 0.770 (2.78) 0.867 (2.16) 0.871 (2.14)
Two-step model 0.839 (2.37) 0.877 (2.10) 0.841 (2.36) 0.889 (1.99) 0.895 (1.94)
  1. Simple models only perform regression, whereas our two-step model performs classification before regression. The columns are different bin-selection strategies, where 'allbins' uses the mean density of all bins, 'TSSbin' uses the two bins flanking the TSS, 'bins.0.2' uses the bins with individual correlation coefficient (r) greater than a threshold (0.2 in this case), 'best5bins' uses the top five bins with the greatest r, and 'bestbin' uses the bin(s) with the greatest r. The values are PCCs (r) between predicted and measured expression levels of PolyA+ cytosolic RNA from K562 cells measured by CAGE, and the values in brackets are RMSE for the predictions.