Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81337
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dc.contributor.authorLan, GQen_US
dc.contributor.authorZhou, JYen_US
dc.contributor.authorXu, RFen_US
dc.contributor.authorLu, Qen_US
dc.contributor.authorWang, HPen_US
dc.date.accessioned2019-09-20T00:55:06Z-
dc.date.available2019-09-20T00:55:06Z-
dc.date.issued2019-
dc.identifier.citationInternational journal of molecular sciences, 2 July 2019, v. 20, no. 14, 3425, p. 1-20en_US
dc.identifier.issn1661-6596-
dc.identifier.urihttp://hdl.handle.net/10397/81337-
dc.description.abstractTranscription 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.en_US
dc.description.sponsorshipDepartment of Computingen_US
dc.language.isoenen_US
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.relation.ispartofInternational journal of molecular sciencesen_US
dc.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/).en_US
dc.rightsThe 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/ijms20143425en_US
dc.subjectTF-binding siteen_US
dc.subjectCross-cell-typeen_US
dc.subjectDeep learningen_US
dc.subjectConvolutional Neural Networken_US
dc.subjectAdversarial Networken_US
dc.titleCross-Cell-Type prediction of TF-binding site by integrating convolutional neural network and adversarial networken_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1-
dc.identifier.epage20-
dc.identifier.volume20-
dc.identifier.issue14-
dc.identifier.doi10.3390/ijms20143425-
dc.identifier.isiWOS:000480449300049-
dc.identifier.scopus2-s2.0-85070458735-
dc.identifier.pmid31336830-
dc.identifier.eissn1422-0067-
dc.identifier.artn3425-
dc.description.validate201909 bcrc-
dc.description.oapublished_final-
Appears in Collections:Journal/Magazine Article
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