Fig. 2
From: McEnhancer: predicting gene expression via semi-supervised assignment of enhancers to target genes
Schematic representation of McEnhancer. Starting with known DHS-gene pairs assigned to a given cluster, it builds a third-order IMM to represent sequence features in DHSs for the positive cluster (colored red). It builds another IMM to represent the null model. In iterative rounds, the model loops on all unlabeled DHSs, calculates log likelihood ratio, and assigns a class label to each unlabeled DHS accordingly. After it finishes looping on all unlabeled DHSs, it adds the newly assigned positive DHSs to the positive cluster and re-estimates IMM parameters. Log likelihood ratios for positively labeled DHSs are re-calculated and re-added to the unlabeled set if their likelihood is low. The model iterates until it converges