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Loop model for learning sequence patterns of insulator loops

TT Tuan Trieu
AM Alexander Martinez-Fundichely
EK Ekta Khurana
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We built DeepMILO by combining learned features of the anchor and anchor orientation models as shown in (Fig. 3b). The output layer of the anchor model was removed, and the two fully connected layers were replaced by two new layers with the same settings to make a new sub-model. Then, the output from this new sub-model was concatenated with outputs from the anchor and anchor orientation model. The loop model uses sequence features of anchors and predicted outcomes from the anchor model and the anchor orientation model for its prediction. In training, validation, and test sets, positive samples are true insulator loop and negative samples consists of non-loop types 1, 2, 3, 4, and 5 with a ratio of 50%:10%:10%:20%:10%, respectively.

Copyright and License information: The Author(s) ©2020
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