Pattern-based bootstrapping framework for biomedical relation extraction

2021 ◽  
Vol 99 ◽  
pp. 104130
Author(s):  
S.S. Deepika ◽  
T.V. Geetha
Keyword(s):  
Relation Extraction ◽  
Biomedical Relation Extraction

scholarly journals Biomedical Relation Extraction Using Distant Supervision

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Nada Boudjellal ◽  
Huaping Zhang ◽  
Asif Khan ◽  
Arshad Ahmad
Keyword(s):  
Big Data ◽  
Information Extraction ◽  
State Of The Art ◽  
Relation Extraction ◽  
Knowledge Bases ◽  
Structured Data ◽  
Distant Supervision ◽  
Future Challenges ◽  
Unstructured Information ◽  
Biomedical Relation Extraction

With the accelerating growth of big data, especially in the healthcare area, information extraction is more needed currently than ever, for it can convey unstructured information into an easily interpretable structured data. Relation extraction is the second of the two important tasks of relation extraction. This study presents an overview of relation extraction using distant supervision, providing a generalized architecture of this task based on the state-of-the-art work that proposed this method. Besides, it surveys the methods used in the literature targeting this topic with a description of different knowledge bases used in the process along with the corpora, which can be helpful for beginner practitioners seeking knowledge on this subject. Moreover, the limitations of the proposed approaches and future challenges were highlighted, and possible solutions were proposed.

Kernel-based learning for biomedical relation extraction

2008 ◽  
Vol 59 (5) ◽  
pp. 756-769 ◽  
Author(s):  
Jiexun Li ◽  
Zhu Zhang ◽  
Xin Li ◽  
Hsinchun Chen
Keyword(s):  
Relation Extraction ◽  
Biomedical Relation Extraction

scholarly journals PPPred: Classifying Protein-phenotype Co-mentions Extracted from Biomedical Literature

2019 ◽  
Author(s):  
Morteza Pourreza Shahri ◽  
Mandi M. Roe ◽  
Gillian Reynolds ◽  
Indika Kahanda
Keyword(s):  
Relation Extraction ◽  
Biomedical Literature ◽  
Supervised Machine Learning ◽  
Human Phenotype ◽  
Unstructured Text ◽  
Gold Standard Dataset ◽  
Sentence Level ◽  
Machine Learning Approach ◽  
Human Proteins ◽  
Biomedical Relation Extraction

ABSTRACTThe MEDLINE database provides an extensive source of scientific articles and heterogeneous biomedical information in the form of unstructured text. One of the most important knowledge present within articles are the relations between human proteins and their phenotypes, which can stay hidden due to the exponential growth of publications. This has presented a range of opportunities for the development of computational methods to extract these biomedical relations from the articles. However, currently, no such method exists for the automated extraction of relations involving human proteins and human phenotype ontology (HPO) terms. In our previous work, we developed a comprehensive database composed of all co-mentions of proteins and phenotypes. In this study, we present a supervised machine learning approach called PPPred (Protein-Phenotype Predictor) for classifying the validity of a given sentence-level co-mention. Using an in-house developed gold standard dataset, we demonstrate that PPPred significantly outperforms several baseline methods. This two-step approach of co-mention extraction and classification constitutes a complete biomedical relation extraction pipeline for extracting protein-phenotype relations.CCS CONCEPTS•Computing methodologies → Information extraction; Supervised learning by classification; •Applied computing →Bioinformatics;

SGAT: a Self-supervised Graph Attention Network for Biomedical Relation Extraction

2021 ◽  
Author(s):  
Qiming Liu ◽  
Zhihao Yang ◽  
Lei Wang ◽  
Yin Zhang ◽  
Hongfei Lin ◽  
...  
Keyword(s):  
Relation Extraction ◽  
Attention Network ◽  
Biomedical Relation Extraction

scholarly journals Exploiting graph kernels for high performance biomedical relation extraction

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Nagesh C. Panyam ◽  
Karin Verspoor ◽  
Trevor Cohn ◽  
Kotagiri Ramamohanarao
Keyword(s):  
High Performance ◽  
Relation Extraction ◽  
Graph Kernels ◽  
Biomedical Relation Extraction
Sign in / Sign up

Export Citation Format

Share Document