Biologically relevant transfer learning improves transcription factor binding prediction.

ABSTRACT:

Background

Deep learning has proven to be a powerful technique for transcription factor (TF) binding prediction but requires large training datasets. Transfer learning can reduce the amount of data required for deep learning, while improving overall model performance, compared to training a separate model for each new task.

Results

We assess a transfer learning strategy for TF binding prediction consisting of a pre-training step, wherein we train a multi-task model with multiple TFs, and a fine-tuning step, wherein we initialize single-task models for individual TFs with the weights learned by the multi-task model, after which the single-task models are trained at a lower learning rate. We corroborate that transfer learning improves model performance, especially if in the pr

SUBMITTER: Novakovsky G 

PROVIDER: S-EPMC8474956 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature