TY - JOUR
T1 - CapsEnhancer
T2 - An Effective Computational Framework for Identifying Enhancers Based on Chaos Game Representation and Capsule Network
AU - Yao, Lantian
AU - Xie, Peilin
AU - Guan, Jiahui
AU - Chung, Chia Ru
AU - Huang, Yixian
AU - Pang, Yuxuan
AU - Wu, Huacong
AU - Chiang, Ying Chih
AU - Lee, Tzong Yi
N1 - Publisher Copyright:
© 2024 The Authors. Published by American Chemical Society.
PY - 2024/7/22
Y1 - 2024/7/22
N2 - Enhancers are a class of noncoding DNA, serving as crucial regulatory elements in governing gene expression by binding to transcription factors. The identification of enhancers holds paramount importance in the field of biology. However, traditional experimental methods for enhancer identification demand substantial human and material resources. Consequently, there is a growing interest in employing computational methods for enhancer prediction. In this study, we propose a two-stage framework based on deep learning, termed CapsEnhancer, for the identification of enhancers and their strengths. CapsEnhancer utilizes chaos game representation to encode DNA sequences into unique images and employs a capsule network to extract local and global features from sequence “images”. Experimental results demonstrate that CapsEnhancer achieves state-of-the-art performance in both stages. In the first and second stages, the accuracy surpasses the previous best methods by 8 and 3.5%, reaching accuracies of 94.5 and 95%, respectively. Notably, this study represents the pioneering application of computer vision methods to enhancer identification tasks. Our work not only contributes novel insights to enhancer identification but also provides a fresh perspective for other biological sequence analysis tasks.
AB - Enhancers are a class of noncoding DNA, serving as crucial regulatory elements in governing gene expression by binding to transcription factors. The identification of enhancers holds paramount importance in the field of biology. However, traditional experimental methods for enhancer identification demand substantial human and material resources. Consequently, there is a growing interest in employing computational methods for enhancer prediction. In this study, we propose a two-stage framework based on deep learning, termed CapsEnhancer, for the identification of enhancers and their strengths. CapsEnhancer utilizes chaos game representation to encode DNA sequences into unique images and employs a capsule network to extract local and global features from sequence “images”. Experimental results demonstrate that CapsEnhancer achieves state-of-the-art performance in both stages. In the first and second stages, the accuracy surpasses the previous best methods by 8 and 3.5%, reaching accuracies of 94.5 and 95%, respectively. Notably, this study represents the pioneering application of computer vision methods to enhancer identification tasks. Our work not only contributes novel insights to enhancer identification but also provides a fresh perspective for other biological sequence analysis tasks.
UR - http://www.scopus.com/inward/record.url?scp=85197433412&partnerID=8YFLogxK
U2 - 10.1021/acs.jcim.4c00546
DO - 10.1021/acs.jcim.4c00546
M3 - Article
C2 - 38946113
AN - SCOPUS:85197433412
SN - 1549-9596
VL - 64
SP - 5725
EP - 5736
JO - Journal of Chemical Information and Modeling
JF - Journal of Chemical Information and Modeling
IS - 14
ER -