Figure 3: Evaluation of enhancer predictions using PEDLA in multiple human cells/tissues.

(A) Evaluation of enhancer prediction using PEDLA in 22 training cell types/tissues. The whole training procedure was repeated with 50 random orders of the 22 training cell types/tissues, and each random order was repeated four times with random permutations of training samples for each cell type/tissue. In total, the training of PEDLA was repeated 200 times on the 22 training cell types/tissues, independently. For each repeat, the trained optimally model of PEDLA was saved for later evaluation for each of the 22 training cell types/tissues. Thus, 22 × 200 = 4,400 optimal models were generated. (1 ≤ j ≤ 22,1 ≤ i ≤ 200) denotes the optimal model that finished training on the j-th training cell type/tissue in the i-th run of the 200 independent runs. (B,C) Performance evaluations of PEDLA in the training cell set and the independent test cell set along the training route. Three performance indicators, accuracy, GM, and F1-score, were assessed for the PEDLA with the trained optimal model in the 22 training cell types/tissues and 20 test cell types/tissues, independently. (B) For a fixed j of the X-axis, all 200 optimal models (1 ≤ i ≤ 200) were used to assess the performance indicators on the 22 training cell types/tissues. The red line represents the mean of the total 200 × 22 = 4,400 values of each performance indicator, and the light blue colour band indicates the 10^th and 90^th percentiles. (C) For a fixed j of the X-axis, all 200 optimal models (1 ≤ i ≤ 200) were used to assess the performance indicators on the 20 test cell types/tissues. The red line represents the mean of the total 200 × 20 = 4,000 values of each performance indicator, and the light blue colour band indicates the 10^th and 90^th percentiles.