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Extracting Biomedical Entity Relations using Biological Interaction Knowledge

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Abstract

Discovering relations of cross-type biomedical entities is crucial for biology research. A large amount of potential or indirect connected biological relations is hidden in millions of biomedical literatures and biological databases. The previous rules-based and deep learning approaches rely on plenty of manual annotations, which is laborious, time-consuming and unsatisfactory. It is necessary to be able to combine available annotated gene databases, chemical, genomic, clinical and other types of data repositories as domain knowledge to assist the extraction of biological entity relations from numerous literatures. Under this scenario, this paper proposes BioGraphSAGE model, a Siamese graph neural network with structured databases as domain knowledge to extract biological entity relations from literatures. Our model combines both biological semantic features and positional features to improve the recognition of relations between distant entities in the same literature. The experiment results show that BioGraphSAGE achieves the best F1 score among other relation extraction models on smaller annotated samples. Moreover, the proposed model can still maintain a F1 score of 0.526 without using annotated training samples.

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Acknowledgements

This work is supported by the Development Project of Jilin Province of China (Nos.20200801033GH, YDZJ202101ZYTS128), Jilin Provincial Key Laboratory of Big Data Intelligent Computing (No.20180622002JC), The Fundamental Research Funds for the Central University, JLU.

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Shuyu Guo, Lan Huang, Ye Wang, Haotian Guan & Tian Bai

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China

    Shuyu Guo, Lan Huang, Ye Wang, Haotian Guan & Tian Bai

  3. Department of Neurology, the Second Hospital of Jilin University, Changchun, 130012, China

    Gang Yao

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  1. Shuyu Guo
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  2. Lan Huang
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  3. Gang Yao
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  4. Ye Wang
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  5. Haotian Guan
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  6. Tian Bai
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Correspondence to Tian Bai.

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Guo, S., Huang, L., Yao, G. et al. Extracting Biomedical Entity Relations using Biological Interaction Knowledge. Interdiscip Sci Comput Life Sci 13, 312–320 (2021). https://doi.org/10.1007/s12539-021-00425-8

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