Using Deep Learning To Recognize Biomedical Entities

被引:0
作者
Yang, Xuemin [1 ]
Huang, Daoyu [1 ]
Zhang, Zhifei [1 ]
Yang, Geng [1 ]
Yang, Ronggen [1 ]
Gong, Lejun [1 ]
机构
[1] Nanjing Univ Posts & Telecommun, Jiangsu Key Lab Big Data Secur & Intelligent Proc, Nanjing 210003, Jiangsu, Peoples R China
来源
2017 12TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS AND KNOWLEDGE ENGINEERING (IEEE ISKE) | 2017年
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
deep learning; entity recognition; distributed word representation; word2vec; neural network; GENE NAME;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With the rapid growth of the high-throughput biological technology, it brings biomedical big omics' data containing literature and annotated data. Especially, a wealth of relevant information exists in various types of biomedical literature. Text mining has emerged as a potential solution to achieve knowledge for bridging between the free text and structured representation of biomedical information. In this work, we used deep learning to recognize biomedical entities. We obtained 84.0% precision, 69.5% recall, and 76.1% F-score aiming at the GENIA corpus, and obtained 91.3% precision, 91.1% recall, and 91.2% F-score aiming at the BioCreAtivE II Gene Mention corpus. Experimental results show that our proposed approach is promising for developing biomedical text mining technology in biomedical entity recognition.
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页数:4
相关论文
共 14 条
[11]   BCC-NER: bidirectional, contextual clues named entity tagger for gene/protein mention recognition [J].
Murugesan, Gurusamy ;
Abdulkadhar, Sabenabanu ;
Bhasuran, Balu ;
Natarajan, Jeyakumar .
Eurasip Journal on Bioinformatics and Systems Biology, 2017, 2017 (01)
[12]   Learning string similarity measures for gene/protein name dictionary look-up using logistic regression [J].
Tsuruoka, Yoshimasa ;
McNaught, John ;
Tsujii, Jun'ichi ;
Ananiadou, Sophia .
BIOINFORMATICS, 2007, 23 (20) :2768-2774
[13]  
Yang RG, 2015, 2015 INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND INFORMATION SYSTEM (SEIS 2015), P713
[14]   Biomedical text mining and its applications in cancer research [J].
Zhu, Fei ;
Patumcharoenpol, Preecha ;
Zhang, Cheng ;
Yang, Yang ;
Chan, Jonathan ;
Meechai, Asawin ;
Vongsangnak, Wanwipa ;
Shen, Bairong .
JOURNAL OF BIOMEDICAL INFORMATICS, 2013, 46 (02) :200-211