Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/82245
Title: MTTFsite : cross-cell type TF binding site prediction by using multi-task learning
Authors: Zhou, JY 
Lu, Q 
Gui, L
Xu, RF
Long, YF 
Wang, HP
Issue Date: 2019
Source: Bioinformatics, 4 June 2019, v. 35, no. 24, p. 5067-5077
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.
Publisher: Oxford University Press
Journal: Bioinformatics 
ISSN: 1367-4803
EISSN: 1460-2059
DOI: 10.1093/bioinformatics/btz451
Rights: ©The Author(s) 2019.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/),which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contactjournals.permissions@oup.com
The following publication 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, Volume 35, Issue 24, 15 December 2019, Pages 5067–5077 is available at https://dx.doi.org/10.1093/bioinformatics/btz451
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