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Table 1 DMR prediction performance of the hybrid model using fivefold cross-validation

From: Predicting environmentally responsive transgenerational differential DNA methylated regions (epimutations) in the genome using a hybrid deep-machine learning approach

Chr #nonDMRs #DMRs Accuracy F1 score Precision Recall
1 13,959 5307 0.9643 0.9450 0.9304 0.9601
2 14,090 3990 0.9815 0.9572 0.9692 0.9456
3 7742 2664 0.9705 0.9467 0.9291 0.9653
4 7199 2900 0.9710 0.9500 0.9314 0.9695
5 7538 2805 0.9639 0.9339 0.9078 0.9616
6 6556 2151 0.9710 0.9459 0.9300 0.9623
7 6636 2349 0.9458 0.8810 0.9096 0.8818
8 4676 1955 0.9617 0.9366 0.9220 0.9517
9 5136 1867 0.9378 0.8906 0.8460 0.9403
10 2728 1804 0.9323 0.9220 0.8756 0.9737
11 3145 1365 0.9498 0.9229 0.9018 0.9451
12 2502 1284 0.9540 0.9405 0.9030 0.9812
13 5471 1789 0.9516 0.9032 0.9042 0.9022
14 5895 1844 0.9647 0.9296 0.9333 0.9260
15 4934 1802 0.9513 0.9157 0.8986 0.9337
16 4286 1500 0.9559 0.9176 0.9073 0.9281
17 3606 1533 0.9274 0.8846 0.8079 0.9777
18 3591 1425 0.9421 0.8995 0.8651 0.9368
19 2108 1195 0.9416 0.9208 0.9042 0.9382
20 1550 1023 0.9440 0.9247 0.8739 0.9810
X 13,664 1699 0.9654 0.8206 0.9096 0.7476
Y 151 79 0.8842 0.8423 0.7246 1.0
All 126,163 44,330 0.9753 0.9502 0.9556 0.9488
  1. For each chromosome, and for ALL chromosomes, the table shows the number of training non-DMRs (#nonDMRs), the number of training DMRs (#DMRs), and the performance metrics for each model: Accuracy, F1 score, Precision and Recall