Drug Protein Interaction Extraction Using SciBERT Based Deep Learning Model

被引：1

作者：

GabAllah, Nada ^{[1
]}

Rafea, Ahmed ^{[1
]}

机构：

[1] Amer Univ Cairo, Comp Sci & Engn Dept, New Cairo, Egypt

来源：

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INNOVATIONS IN COMPUTING RESEARCH (ICR'22) | 2022年 / 1431卷

关键词：

Relation extraction; Drug protection; Biomedical; Deep learning; SciBERT;

D O I：

10.1007/978-3-031-14054-9_16

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Information extraction from textual data is becoming more crucial with the increase of available data on the internet. Automatic extraction of information from biomedical data is very useful to researchers, saving time and effort exerted by them. Relation extraction between medical entities is one of the active research areas. In this paper we are presenting a relation extraction deep learning model based on SciBERT, to extract relations between drugs/chemicals and proteins/genes entities from PubMed literature. The model could achieve an average micro F1 score of 91.75% on the ChemProt test set.

引用

页码：157 / 165

页数：9

共 21 条

[1] Extraction of chemical-protein interactions from the literature using neural networks and narrow instance representation
Antunes, Rui
Matos, Sergio
[J]. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION, 2019,
[2] Beltagy I, 2019, 2019 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING AND THE 9TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (EMNLP-IJCNLP 2019), P3615
[3] Choi D, 2020, FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, EMNLP 2020, P2086
[4] Devlin J, 2019, Arxiv, DOI [arXiv:1810.04805, DOI 10.48550/ARXIV.1810.04805]
[5] Grishman R., 1997, Information Extraction. A Multidisciplinary Approach to an Emerging Information Technology International Summer School, SCIE-97, P10
[6] Guo ZJ, 2020, Arxiv, DOI arXiv:1906.07510
[7] Khoo CSG, 2000, 38TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P336
[8] Krallinger M., 2017, Overview of the BioCreative VI chemical-Protein Interaction Track
[9] Chemical-gene relation extraction using recursive neural network
Lim, Sangrak
Kang, Jaewoo
[J]. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION, 2018,
[10] Liu S, ATTENTION BASED NEUR, P4

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