GMFR-CNN: An Integration of Gapped Motif Feature Representation and Deep Learning Approach for Enhancer Prediction

Unravelling gene expression has become a critical procedure in bioinformatics world today and required continuous efforts to form a complete picture of enhancers. Enhancers are explicit patterns of gene expression that bound by activators to stimulate transcription. It could reside in upstream or d...

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Main Authors:	Yu, Shiong Wong, Nung, Kion Lee, Norshafarina, Omar
Format:	Article
Language:	English
Published:	ACM Digital Library 2016
Subjects:	T Technology (General)
Online Access:	http://ir.unimas.my/id/eprint/15731/ http://ir.unimas.my/id/eprint/15731/1/GMFR-CNN%20An%20Integration%20of%20Gapped%20Motif%20%28abstract%29.pdf

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author	Yu, Shiong Wong Nung, Kion Lee Norshafarina, Omar
author_facet	Yu, Shiong Wong Nung, Kion Lee Norshafarina, Omar
author_sort	Yu, Shiong Wong
building	UNIMAS Institutional Repository
collection	Online Access
description	Unravelling gene expression has become a critical procedure in bioinformatics world today and required continuous efforts to form a complete picture of enhancers. Enhancers are explicit patterns of gene expression that bound by activators to stimulate transcription. It could reside in upstream or downstream thousands of base pairs away without any fixed position. Therefore, the identification task of enhancers is extremely challenging. The inclusion of gaps in motif identification improved the overall accuracy and sensitivity, however, this feature is not fully utilised in deep learning method yet. Deep learning, is a powerful machine learning technique that has been actively used in image recognition and this technique has begun to shed light in bioinformatics. The expressiveness of deep learning enables higher feature learning from lower level ones. As a result, an integration of gapped motif feature representation (GMFR) and deep learning approach called deep convolutional neural networks (CNNs) is introduced to improve the predictive power of enhancer prediction. We called this method as GMFR-CNN. Comparative studies indicate that GMFR-CNN outperforms the other deep learning and gapped k-mer SVM tools with average 98% prediction accuracy. Breakthrough in deep learning technique certainly improves the performance in the near future.
first_indexed	2025-11-15T06:47:13Z
format	Article
id	unimas-15731
institution	Universiti Malaysia Sarawak
institution_category	Local University
language	English
last_indexed	2025-11-15T06:47:13Z
publishDate	2016
publisher	ACM Digital Library
recordtype	eprints
repository_type	Digital Repository
spelling	unimas-157312017-03-30T06:36:03Z http://ir.unimas.my/id/eprint/15731/ GMFR-CNN: An Integration of Gapped Motif Feature Representation and Deep Learning Approach for Enhancer Prediction Yu, Shiong Wong Nung, Kion Lee Norshafarina, Omar T Technology (General) Unravelling gene expression has become a critical procedure in bioinformatics world today and required continuous efforts to form a complete picture of enhancers. Enhancers are explicit patterns of gene expression that bound by activators to stimulate transcription. It could reside in upstream or downstream thousands of base pairs away without any fixed position. Therefore, the identification task of enhancers is extremely challenging. The inclusion of gaps in motif identification improved the overall accuracy and sensitivity, however, this feature is not fully utilised in deep learning method yet. Deep learning, is a powerful machine learning technique that has been actively used in image recognition and this technique has begun to shed light in bioinformatics. The expressiveness of deep learning enables higher feature learning from lower level ones. As a result, an integration of gapped motif feature representation (GMFR) and deep learning approach called deep convolutional neural networks (CNNs) is introduced to improve the predictive power of enhancer prediction. We called this method as GMFR-CNN. Comparative studies indicate that GMFR-CNN outperforms the other deep learning and gapped k-mer SVM tools with average 98% prediction accuracy. Breakthrough in deep learning technique certainly improves the performance in the near future. ACM Digital Library 2016 Article PeerReviewed text en http://ir.unimas.my/id/eprint/15731/1/GMFR-CNN%20An%20Integration%20of%20Gapped%20Motif%20%28abstract%29.pdf Yu, Shiong Wong and Nung, Kion Lee and Norshafarina, Omar (2016) GMFR-CNN: An Integration of Gapped Motif Feature Representation and Deep Learning Approach for Enhancer Prediction. CSBio '16 Proceedings of the 7th International Conference on Computational Systems-Biology and Bioinformatics. ISSN ISBN: 978-1-4503-4794-5 http://dl.acm.org/citation.cfm?id=3029380 doi>10.1145/3029375.3029380
spellingShingle	T Technology (General) Yu, Shiong Wong Nung, Kion Lee Norshafarina, Omar GMFR-CNN: An Integration of Gapped Motif Feature Representation and Deep Learning Approach for Enhancer Prediction
title	GMFR-CNN: An Integration of Gapped Motif Feature Representation and Deep Learning Approach for Enhancer Prediction
title_full	GMFR-CNN: An Integration of Gapped Motif Feature Representation and Deep Learning Approach for Enhancer Prediction
title_fullStr	GMFR-CNN: An Integration of Gapped Motif Feature Representation and Deep Learning Approach for Enhancer Prediction
title_full_unstemmed	GMFR-CNN: An Integration of Gapped Motif Feature Representation and Deep Learning Approach for Enhancer Prediction
title_short	GMFR-CNN: An Integration of Gapped Motif Feature Representation and Deep Learning Approach for Enhancer Prediction
title_sort	gmfr-cnn: an integration of gapped motif feature representation and deep learning approach for enhancer prediction
topic	T Technology (General)
url	http://ir.unimas.my/id/eprint/15731/ http://ir.unimas.my/id/eprint/15731/ http://ir.unimas.my/id/eprint/15731/ http://ir.unimas.my/id/eprint/15731/1/GMFR-CNN%20An%20Integration%20of%20Gapped%20Motif%20%28abstract%29.pdf

GMFR-CNN: An Integration of Gapped Motif Feature Representation and Deep Learning Approach for Enhancer Prediction

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