MAResNet: predicting transcription factor binding sites by combining multi-scale bottom-up and top-down attention and residual network.

ABSTRACT: Accurate identification of transcription factor binding sites is of great significance in understanding gene expression, biological development and drug design. Although a variety of methods based on deep-learning models and large-scale data have been developed to predict transcription factor binding sites in DNA sequences, there is room for further improvement in prediction performance. In addition, effective interpretation of deep-learning models is greatly desirable. Here we present MAResNet, a new deep-learning method, for predicting transcription factor binding sites on 690 ChIP-seq datasets. More specifically, MAResNet combines the bottom-up and top-down attention mechanisms and a state-of-the-art feed-forward network (ResNet), which is constructed by stacking attention modules that

SUBMITTER: Han K 

PROVIDER: S-EPMC8769703 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature