Deep learning-based large-scale named entity recognition for anatomical region of mammalian brain

作     者：Xiaokang Chai Yachao Di Zhao Feng Yue Guan Guoqing Zhang Anan Li Qingming Luo Xiaokang Chai;Yachao Di;Zhao Feng;Yue Guan;Guoqing Zhang;Anan Li;Qingming Luo

作者机构：Britton Chance Center for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsMoE Key Laboratory for Biomedical PhotonicsHuazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Biomedical Engineering of Hainan ProvinceSchool of Biomedical EngineeringHainan UniversityHaikou 570228China CAS Key Laboratory of Computational BiologyBio-Med Big Data CenterShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesShanghai 200031China Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST-Suzhou Institute for BrainsmaticsJITRISuzhou 215123China CAS Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai 200031China 

出 版 物：《Quantitative Biology》 (定量生物学（英文版）)

年 卷 期：2022年第10卷第3期

页      面：253-263页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by the National Science and Technology Innovation 2030 Grant(No.2021ZD0201002) the National Natural Science Foundation of China(Nos.T2122015 and 61890954) CAMS Innovation Fund for Medical Sciences(No.2019-I2M-5-014) Suzhou Prospective Application Research Project(No.SYG201915) 

主　　题：brain region entity extraction literature mining WhiteText deep learning 

摘      要：Background:Images of anatomical regions and neuron type distribution,as well as their related literature are valuable assets for neuroscience *** are vital evidence and vehicles in discovering new phenomena and knowledge refinement through image and text big *** knowledge acquired from image data generally echoes with the literature accumulated over the *** knowledge within the literature can provide a comprehensive context for a deeper understanding of the image ***,it is quite a challenge to manually identify the related literature and summarize the neuroscience knowledge in the large-scale ***,neuroscientists are in dire need of an automated method to extract neuroscience knowledge from large-scale ***:A proposed deep learning model named BioBERT-CRF extracts brain region entities from the WhiteText *** model takes advantage of BioBERT and CRF to predict entity labels while ***:The proposed deep learning model demonstrated comparable performance against or even outperforms the previous models on the WhiteText *** BioBERT-CRF model has achieved the best average precision,recall,and F1 score of 81.3%,84.0%,and 82.6%,*** used the BioBERT-CRF model to predict brain region entities in a large-scale PubMed abstract dataset and used a rule-based method to normalize all brain region entities to three neuroscience ***:Our work shows that the BioBERT-CRF model can be well-suited for brain region entity *** rankings of different brain region entities by their appearance in the large-scale corpus indicate the anatomical regions that researchers are most concerned about.