A Knowledge Graph based Bidirectional Recurrent Neural Network Method for Literature-based Discovery

Shengtian Sang; Zhihao Yang; Xiaoxia Liu; Lei Wang; Yin Zhang; Hongfei Lin; Jian Wang; Liang Yang; Kan Xu; Yijia Zhang

doi:10.1109/BIBM.2018.8621423

A Knowledge Graph based Bidirectional Recurrent Neural Network Method for Literature-based Discovery

Shengtian Sang
, Zhihao Yang
, Xiaoxia Liu
, Lei Wang
, Yin Zhang
, Hongfei Lin
, Jian Wang
, Liang Yang
, Kan Xu
, Yijia Zhang

Department of Biomedical Informatics

Dalian University of Technology
Beijing Institute of Health Administration and Medical Information

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

Abstract

In this paper, we present a model which incorporates biomedical knowledge graph, graph embedding and deep learning methods for literature-based discovery. Firstly, the relations between entities are extracted from biomedical abstracts and then a knowledge graph is constructed by using these obtained relations. Secondly, the graph embedding technologies are applied to convert the entities and relations in the knowledge graph into a low-dimensional vector space. Thirdly, a bidirectional Long Short-Term Memory network is trained based on the entity associations represented by the pre-trained graph embeddings. Finally, the learned model is used for open and closed literature-based discovery tasks. The experimental results show that our method could not only effectively discover hidden associations between entities, but also reveal the corresponding mechanism of interactions. It suggests that incorporating knowledge graph and deep learning methods is an effective way for capturing the underlying complex associations between entities hidden in the literature.

Original language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Editors	Harald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	751-752
Number of pages	2
ISBN (Electronic)	9781538654880
DOIs	https://doi.org/10.1109/BIBM.2018.8621423
State	Published - Jan 21 2019
Event	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain Duration: Dec 3 2018 → Dec 6 2018

Publication series

Name	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Country/Territory	Spain
City	Madrid
Period	12/3/18 → 12/6/18

Keywords

bidirectional recurrent neural network
drug discovery
knowledge graph
literature-based discovery

Access to Document

10.1109/BIBM.2018.8621423

Cite this

Sang, S., Yang, Z., Liu, X., Wang, L., Zhang, Y., Lin, H., Wang, J., Yang, L., Xu, K., & Zhang, Y. (2019). A Knowledge Graph based Bidirectional Recurrent Neural Network Method for Literature-based Discovery. In H. Schmidt, D. Griol, H. Wang, J. Baumbach, H. Zheng, Z. Callejas, X. Hu, J. Dickerson, & L. Zhang (Eds.), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 (pp. 751-752). Article 8621423 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2018.8621423

Sang, Shengtian ; Yang, Zhihao ; Liu, Xiaoxia et al. / A Knowledge Graph based Bidirectional Recurrent Neural Network Method for Literature-based Discovery. Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. editor / Harald Schmidt ; David Griol ; Haiying Wang ; Jan Baumbach ; Huiru Zheng ; Zoraida Callejas ; Xiaohua Hu ; Julie Dickerson ; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 751-752 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

@inproceedings{f2fa8d36fec848c8b1e652b82cc0fb89,

title = "A Knowledge Graph based Bidirectional Recurrent Neural Network Method for Literature-based Discovery",

abstract = "In this paper, we present a model which incorporates biomedical knowledge graph, graph embedding and deep learning methods for literature-based discovery. Firstly, the relations between entities are extracted from biomedical abstracts and then a knowledge graph is constructed by using these obtained relations. Secondly, the graph embedding technologies are applied to convert the entities and relations in the knowledge graph into a low-dimensional vector space. Thirdly, a bidirectional Long Short-Term Memory network is trained based on the entity associations represented by the pre-trained graph embeddings. Finally, the learned model is used for open and closed literature-based discovery tasks. The experimental results show that our method could not only effectively discover hidden associations between entities, but also reveal the corresponding mechanism of interactions. It suggests that incorporating knowledge graph and deep learning methods is an effective way for capturing the underlying complex associations between entities hidden in the literature.",

keywords = "bidirectional recurrent neural network, drug discovery, knowledge graph, literature-based discovery",

author = "Shengtian Sang and Zhihao Yang and Xiaoxia Liu and Lei Wang and Yin Zhang and Hongfei Lin and Jian Wang and Liang Yang and Kan Xu and Yijia Zhang",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 ; Conference date: 03-12-2018 Through 06-12-2018",

year = "2019",

month = jan,

day = "21",

doi = "10.1109/BIBM.2018.8621423",

language = "English",

series = "Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "751--752",

editor = "Harald Schmidt and David Griol and Haiying Wang and Jan Baumbach and Huiru Zheng and Zoraida Callejas and Xiaohua Hu and Julie Dickerson and Le Zhang",

booktitle = "Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018",

address = "United States",

}

Sang, S, Yang, Z, Liu, X, Wang, L, Zhang, Y, Lin, H, Wang, J, Yang, L, Xu, K & Zhang, Y 2019, A Knowledge Graph based Bidirectional Recurrent Neural Network Method for Literature-based Discovery. in H Schmidt, D Griol, H Wang, J Baumbach, H Zheng, Z Callejas, X Hu, J Dickerson & L Zhang (eds), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018., 8621423, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Institute of Electrical and Electronics Engineers Inc., pp. 751-752, 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Madrid, Spain, 12/3/18. https://doi.org/10.1109/BIBM.2018.8621423

A Knowledge Graph based Bidirectional Recurrent Neural Network Method for Literature-based Discovery. / Sang, Shengtian; Yang, Zhihao; Liu, Xiaoxia et al.
Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. ed. / Harald Schmidt; David Griol; Haiying Wang; Jan Baumbach; Huiru Zheng; Zoraida Callejas; Xiaohua Hu; Julie Dickerson; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. p. 751-752 8621423 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Knowledge Graph based Bidirectional Recurrent Neural Network Method for Literature-based Discovery

AU - Sang, Shengtian

AU - Yang, Zhihao

AU - Liu, Xiaoxia

AU - Wang, Lei

AU - Zhang, Yin

AU - Lin, Hongfei

AU - Wang, Jian

AU - Yang, Liang

AU - Xu, Kan

AU - Zhang, Yijia

PY - 2019/1/21

Y1 - 2019/1/21

N2 - In this paper, we present a model which incorporates biomedical knowledge graph, graph embedding and deep learning methods for literature-based discovery. Firstly, the relations between entities are extracted from biomedical abstracts and then a knowledge graph is constructed by using these obtained relations. Secondly, the graph embedding technologies are applied to convert the entities and relations in the knowledge graph into a low-dimensional vector space. Thirdly, a bidirectional Long Short-Term Memory network is trained based on the entity associations represented by the pre-trained graph embeddings. Finally, the learned model is used for open and closed literature-based discovery tasks. The experimental results show that our method could not only effectively discover hidden associations between entities, but also reveal the corresponding mechanism of interactions. It suggests that incorporating knowledge graph and deep learning methods is an effective way for capturing the underlying complex associations between entities hidden in the literature.

AB - In this paper, we present a model which incorporates biomedical knowledge graph, graph embedding and deep learning methods for literature-based discovery. Firstly, the relations between entities are extracted from biomedical abstracts and then a knowledge graph is constructed by using these obtained relations. Secondly, the graph embedding technologies are applied to convert the entities and relations in the knowledge graph into a low-dimensional vector space. Thirdly, a bidirectional Long Short-Term Memory network is trained based on the entity associations represented by the pre-trained graph embeddings. Finally, the learned model is used for open and closed literature-based discovery tasks. The experimental results show that our method could not only effectively discover hidden associations between entities, but also reveal the corresponding mechanism of interactions. It suggests that incorporating knowledge graph and deep learning methods is an effective way for capturing the underlying complex associations between entities hidden in the literature.

KW - bidirectional recurrent neural network

KW - drug discovery

KW - knowledge graph

KW - literature-based discovery

UR - https://www.scopus.com/pages/publications/85062573619

U2 - 10.1109/BIBM.2018.8621423

DO - 10.1109/BIBM.2018.8621423

M3 - Conference contribution

AN - SCOPUS:85062573619

T3 - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

SP - 751

EP - 752

BT - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

A2 - Schmidt, Harald

A2 - Griol, David

A2 - Wang, Haiying

A2 - Baumbach, Jan

A2 - Zheng, Huiru

A2 - Callejas, Zoraida

A2 - Hu, Xiaohua

A2 - Dickerson, Julie

A2 - Zhang, Le

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Y2 - 3 December 2018 through 6 December 2018

ER -

Sang S, Yang Z, Liu X, Wang L, Zhang Y, Lin H et al. A Knowledge Graph based Bidirectional Recurrent Neural Network Method for Literature-based Discovery. In Schmidt H, Griol D, Wang H, Baumbach J, Zheng H, Callejas Z, Hu X, Dickerson J, Zhang L, editors, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 751-752. 8621423. (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). doi: 10.1109/BIBM.2018.8621423

A Knowledge Graph based Bidirectional Recurrent Neural Network Method for Literature-based Discovery

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this