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Table 6 Comparison of effects of models using different pre-training loss

From: LTPConstraint: a transfer learning based end-to-end method for RNA secondary structure prediction

Loss

Pre-train

Train

Precision

Recall

F1-score

Precision

Recall

F1-score

PRAUC-loss

0.0351

0.6177

0.0665

0.8599

0.7897

0.8233

Negative-F1

0.1564

0.0987

0.1210

0.3587

0.2107

0.2655

Weighed-logistic

0.0283

0.7559

0.0546

0.8963

0.8015

0.8462

No-pretraining

–

–

–

0.8907

0.5861

0.7070

  1. In the pre-training phase, the hard constraint layer is removed from the network and different loss functions are used for training. In the training phase, we used different pre-trained models for transfer learning to obtain prediction models on the Rfam dataset. The loss function of training phase is Neagtive-F1 function
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BMC Bioinformatics

ISSN: 1471-2105

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