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MTTFsite : cross-cell-type TF binding site prediction by using multi-task learning
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Zhou, Jiyun, Lu, Qin, Gui, Lin, Xu, Ruifeng, Long, Yunfei and Wang, Hongpeng (2019) MTTFsite : cross-cell-type TF binding site prediction by using multi-task learning. Bioinformatics, 35 (24). pp. 5067-5077. doi:10.1093/bioinformatics/btz451 ISSN 1367-4803.
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WRAP-MTTFsite-cross-cell-type-TF-binding-site-prediction-using-multi-task-learning-Gui-2019.pdf - Accepted Version - Requires a PDF viewer. Download (677Kb) | Preview |
Official URL: https://doi.org/10.1093/bioinformatics/btz451
Abstract
Motivation
The prediction of transcription factor binding sites (TFBSs) is crucial for gene expression analysis. Supervised learning approaches for TFBS predictions require large amounts of labeled data. However, many TFs of certain cell types either do not have sufficient labeled data or do not have any labeled data.
Results
In this paper, a multi-task learning framework (called MTTFsite) is proposed to address the lack of labeled data problem by leveraging on labeled data available in cross-cell types. The proposed MTTFsite contains a shared CNN to learn common features for all cell types and a private CNN for each cell type to learn private features. The common features are aimed to help predicting TFBSs for all cell types especially those cell types that lack labeled data. MTTFsite is evaluated on 241 cell type TF pairs and compared with a baseline method without using any multi-task learning model and a fully shared multi-task model that uses only a shared CNN and do not use private CNNs. For cell types with insufficient labeled data, results show that MTTFsite performs better than the baseline method and the fully shared model on more than 89% pairs. For cell types without any labeled data, MTTFsite outperforms the baseline method and the fully shared model by more than 80 and 93% pairs, respectively. A novel gene expression prediction method (called TFChrome) using both MTTFsite and histone modification features is also presented. Results show that TFBSs predicted by MTTFsite alone can achieve good performance. When MTTFsite is combined with histone modification features, a significant 5.7% performance improvement is obtained.
Item Type: | Journal Article | ||||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||
Journal or Publication Title: | Bioinformatics | ||||||||
Publisher: | Oxford University Press | ||||||||
ISSN: | 1367-4803 | ||||||||
Official Date: | 15 December 2019 | ||||||||
Dates: |
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Volume: | 35 | ||||||||
Number: | 24 | ||||||||
Page Range: | pp. 5067-5077 | ||||||||
DOI: | 10.1093/bioinformatics/btz451 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Reuse Statement (publisher, data, author rights): | This is a pre-copyedited, author-produced version of an article accepted for publication in Bioinformatics following peer review. The version of record Jiyun Zhou, Qin Lu, Lin Gui, Ruifeng Xu, Yunfei Long, Hongpeng Wang, MTTFsite: cross-cell type TF binding site prediction by using multi-task learning, Bioinformatics, , btz451, is available online at: https://doi.org/10.1093/bioinformatics/btz451 | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Date of first compliant deposit: | 31 May 2019 | ||||||||
Date of first compliant Open Access: | 4 June 2020 | ||||||||
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