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| Disease Knowledge Discovery Based on SPO Predications |
Cai Miaozhi,Li Xiaoying,Zhao Jiawei,Feng Fengxiang,Ren Huiling( ) |
| Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100020, China |
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Abstract [Objective] This study tries to discover knowledge from the high-level evidence-based literature on diseases indexed by PubMed, aiming to provide reference for clinical diagnosis, treatment, as well as routine prevention and control of diseases. [Methods] We proposed a diseases knowledge discovery model based on SPO predications with the semantic extraction tool SemRep. Then we selected the diabetes-related literature to evaluate this model, and discovered knowledge based on SPO visualization and clinical knowledge. [Results] We obtained 1 258 SPO predications and 16 semantic relationships, which identified diabetes-related genes, common complications, as well as detection and treatment methods. [Limitations] We only examined our model with publicly accessible literature. More research is needed to include knowledge bases and electronic medical records. [Conclusions] The disease knowledge discovery model based on SPO predication could identify the biomedical knowledge from literature, which provides potential research hypotheses and ideas for biomedical researchers.
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Received: 21 June 2021
Published: 22 February 2022
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| Fund:National Key Research and Development Program of China(2019AAA0104901);National Social Science Fund of China(20BTQ062);China-WHO Biennial Collaborative Projects(GJ2-2021-WHOSO-01) |
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Corresponding Authors:
Ren Huiling，ORCID：0000-0002-1067-408X
E-mail: wangjd@sic.gov.cn
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