Figure 2From: Enhancer identification in mouse embryonic stem cells using integrative modeling of chromatin and genomic featuresFeature ranking determined using multinomial logistic regression with LASSO regularization. (A) Feature weights in each class with respect to log lambda, which is a penalty factor to shrink feature weights. Weights of features less discriminative of the three categories shrink to 0 as lambda is increased. Top ranking features are those with non-zero weights at high lambda values: p300, H3K4me1 and MED12 for Enh group; CpG islands, H3K4me3, G + C percent and RNAPII-ser5 for PrL groups. (B) Signatures used in the model chosen from cross validation (log lambda = −4) are shown in each category with their degree of contribution.Back to article page