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A hybrid deep learning framework for bacterial named entity recognition with domain features

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成果类型:
期刊论文、会议论文
作者:
Li, Xusheng;Fu, Chengcheng;Zhong, Ran;Zhong, Duo;He, Tingting(何婷婷);...
通讯作者:
Jiang, Xingpeng(蒋兴鹏
作者机构:
[Jiang, Xingpeng; He, Tingting; Fu, Chengcheng; Li, Xusheng; Zhong, Duo] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.
[Jiang, Xingpeng; He, Tingting; Fu, Chengcheng; Li, Xusheng; Zhong, Duo] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Hubei, Peoples R China.
[Zhong, Ran] Cent China Normal Univ, Collaborat & Innovat Ctr, Wuhan, Hubei, Peoples R China.
通讯机构:
[Jiang, Xingpeng] C
Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.
Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Hubei, Peoples R China.
语种:
英文
关键词:
Named entity recognition;Biomedical text mining;Conditional random field;Deep learning
期刊:
BMC Bioinformatics
ISSN:
1471-2105
年:
2019
卷:
20
期:
Suppl 16
页码:
583
会议名称:
IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Bioinformatics and Systems Biology
会议时间:
DEC 03-06, 2018
会议地点:
Madrid, SPAIN
会议主办单位:
[Li, Xusheng;Fu, Chengcheng;Zhong, Duo;He, Tingting;Jiang, Xingpeng] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.^[Li, Xusheng;Fu, Chengcheng;Zhong, Duo;He, Tingting;Jiang, Xingpeng] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Hubei, Peoples R China.^[Zhong, Ran] Cent China Normal Univ, Collaborat & Innovat Ctr, Wuhan, Hubei, Peoples R China.
会议赞助商:
IEEE
出版地:
CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
出版者:
BMC
基金类别:
The research was supported by the National Key Research and Development Program of China (2017YFC0909502), the National Natural Science Foundation of China (61532008 and 61872157). Specifically, the publication costs are funded by the National Key Research and Development Program of China (2017YFC0909502).
机构署名:
本校为第一且通讯机构
院系归属:
计算机学院
摘要:
Background: Microbes have been shown to play a crucial role in various ecosystems. Many human diseases have been proved to be associated with bacteria, so it is essential to extract the interaction between bacteria for medical research and application. At the same time, many bacterial interactions with certain experimental evidences have been reported in biomedical literature. Integrating this knowledge into a database or knowledge graph could accelerate the progress of biomedical research. A crucial and necessary step in interaction extraction...

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