MAResNet: predicting transcription factor binding sites by combining multi-scale bottom-up and top-down attention and residual network
| العنوان: | MAResNet: predicting transcription factor binding sites by combining multi-scale bottom-up and top-down attention and residual network |
|---|---|
| المؤلفون: | Jiangning Song, Ke Han, Yi-Heng Zhu, Jian Xu, Dong-Jun Yu, Long-Chen Shen |
| المصدر: | Brief Bioinform |
| بيانات النشر: | Oxford University Press (OUP), 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Computer science, Sequence analysis, Computational biology, Residual, 03 medical and health sciences, Deep Learning, 0302 clinical medicine, Molecular Biology, 030304 developmental biology, 0303 health sciences, Binding Sites, Mechanism (biology), business.industry, Deep learning, Top-down and bottom-up design, DNA binding site, Identification (information), Problem Solving Protocol, Neural Networks, Computer, Artificial intelligence, Scale (map), business, 030217 neurology & neurosurgery, Protein Binding, Transcription Factors, Information Systems |
| الوصف: | 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 generate attention-aware features. In particular, the multi-scale attention mechanism is utilized at the first stage to extract rich and representative sequence features. We further discuss the attention-aware features learned from different attention modules in accordance with the changes as the layers go deeper. The features learned by MAResNet are also visualized through the TMAP tool to illustrate that the method can extract the unique characteristics of transcription factor binding sites. The performance of MAResNet is extensively tested on 690 test subsets with an average AUC of 0.927, which is higher than that of the current state-of-the-art methods. Overall, this study provides a new and useful framework for the prediction of transcription factor binding sites by combining the funnel attention modules with the residual network. |
| تدمد: | 1477-4054 1467-5463 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b9d39b524a222c0c1dd9dec4c3c7bff3 https://doi.org/10.1093/bib/bbab445 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....b9d39b524a222c0c1dd9dec4c3c7bff3 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 14774054 14675463 |
|---|