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iEnhancer-BERT: A Novel Transfer Learning Architecture Based on DNA-Language Model for Identifying Enhancers and Their Strength

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成果类型:
会议论文
作者:
Luo, Hanyu;Chen, Cheng;Shan, Wenyu;Ding, Pingjian;Luo, Lingyun
作者机构:
[Shan, Wenyu; Chen, Cheng; Luo, Lingyun; Ding, Pingjian; Luo, Hanyu] Univ South China, Sch Comp Sci, Hengyang 421001, Hunan, Peoples R China.
[Luo, Lingyun] Hunan Med Big Data Int Sci & Technol Innovat Coop, Hengyang 421001, Peoples R China.
语种:
英文
关键词:
Enhancer identification;DNA sequence;BERT;Transfer learning;Convolutional neural network
期刊:
Lecture Notes in Computer Science
ISSN:
0302-9743
年:
2022
卷:
13394
页码:
153-165
会议名称:
18th International Conference on Intelligent Computing (ICIC)
会议论文集名称:
Lecture Notes in Computer Science
会议时间:
AUG 07-11, 2022
会议地点:
Xian, PEOPLES R CHINA
会议主办单位:
[Luo, Hanyu;Chen, Cheng;Shan, Wenyu;Ding, Pingjian;Luo, Lingyun] Univ South China, Sch Comp Sci, Hengyang 421001, Hunan, Peoples R China.^[Luo, Lingyun] Hunan Med Big Data Int Sci & Technol Innovat Coop, Hengyang 421001, Peoples R China.
主编:
Huang, DS Jo, KH Jing, J Premaratne, P Bevilacqua, V Hussain, A
出版地:
GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
出版者:
SPRINGER INTERNATIONAL PUBLISHING AG
ISBN:
978-3-031-13829-4; 978-3-031-13828-7
基金类别:
National Natural Science Foundation of China [62002154]; Hunan Provincial Natural Science Foundation of China [2019JJ50520, 2021JJ40467]; Research Foundation of Hunan Educational Committee [20C1579]; Scientific Research Startup Foundation of University of South China [190XQD096]
机构署名:
本校为第一机构
摘要:
Enhancers are small segments of DNA that bind to proteins (transcription factors) and the transcription of a gene is strengthened after binding to the protein, thus playing an essential role in gene expression. Recently, machine learning-based methods have become a trend in identifying enhancers and their strength. In this study, we propose iEnhancer-BERT, a novel transfer learning method based on pre-trained DNA language model using the whole human genome. More specifically, iEnhancer-BERT consists of a BERT layer for feature extraction and a CNN layer for classification. We initialize our pa...

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