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DeepCLIP uses ADAM (47) () for gradient descent optimization. For the BLSTM layer, the parameters are sampled from a Gaussian distribution with
and
. DeepCLIP uses binary cross entropy as loss function and employs dropout in order to avoid overfitting. The most optimal network weights based on Area Under Receiver Operator Curve (AUROC) performance on the validation set are saved during training. Dropout is applied to the BLSTM layer (10%).
Copyright and License information: ©2020 The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
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