Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81337
Title: Cross-Cell-Type prediction of TF-binding site by integrating convolutional neural network and adversarial network
Authors: Lan, GQ
Zhou, JY
Xu, RF
Lu, Q 
Wang, HP
Keywords: TF-binding site
Cross-cell-type
Deep learning
Convolutional Neural Network
Adversarial Network
Issue Date: 2019
Publisher: Molecular Diversity Preservation International (MDPI)
Source: International journal of molecular sciences, 2 July 2019, v. 20, no. 14, 3425, p. 1-20 How to cite?
Journal: International journal of molecular sciences 
Abstract: Transcription factor binding sites (TFBSs) play an important role in gene expression regulation. Many computational methods for TFBS prediction need sufficient labeled data. However, many transcription factors (TFs) lack labeled data in cell types. We propose a novel method, referred to as DANN TF, for TFBS prediction. DANN TF consists of a feature extractor, a label predictor, and a domain classifier. The feature extractor and the domain classifier constitute an Adversarial Network, which ensures that learned features are common features across different cell types. DANN TF is evaluated on five TFs in five cell types with a total of 25 cell-type TF pairs and compared to a baseline method which does not use Adversarial Network. For both data augmentation and cross-cell-type prediction, DANN TF performs better than the baseline method on most cell-type TF pairs. DANN TF is further evaluated by an additional 13 TFs in the five cell types with a total of 65 cell-type TF pairs. Results show that DANN TF achieves significantly higher AUC than the baseline method on 96.9% pairs of the 65 cell-type TF pairs. This is a strong indication that DANN TF can indeed learn common features for cross-cell-type TFBS prediction.
URI: http://hdl.handle.net/10397/81337
ISSN: 1661-6596
EISSN: 1422-0067
DOI: 10.3390/ijms20143425
Rights: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The following publication Lan, G.; Zhou, J.; Xu, R.; Lu, Q.; Wang, H. Cross-Cell-Type Prediction of TF-Binding Site by Integrating Convolutional Neural Network and Adversarial Network. Int. J. Mol. Sci. 2019, 20, 3425, 1-20 is available at https://dx.doi.org/10.3390/ijms20143425
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