This paper is in the following e-collection/theme issue:

Natural Language Processing (692) Machine Learning (1465)

Published on in Vol 7, No 4 (2019): Oct-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/14502, first published .
Using a Large Margin Context-Aware Convolutional Neural Network to Automatically Extract Disease-Disease Association from Literature: Comparative Analytic Study

Using a Large Margin Context-Aware Convolutional Neural Network to Automatically Extract Disease-Disease Association from Literature: Comparative Analytic Study

Using a Large Margin Context-Aware Convolutional Neural Network to Automatically Extract Disease-Disease Association from Literature: Comparative Analytic Study

Authors of this article:

Po-Ting Lai1 Author Orcid Image ;   Wei-Liang Lu2 Author Orcid Image ;   Ting-Rung Kuo2 Author Orcid Image ;   Chia-Ru Chung2 Author Orcid Image ;   Jen-Chieh Han2 Author Orcid Image ;   Richard Tzong-Han Tsai2 Author Orcid Image ;   Jorng-Tzong Horng2, 3 Author Orcid Image
Article Authors Cited by (4) Tweetations (3) Metrics

Journals

  1. Karapetian K, Jeon S, Kwon J, Suh Y. Supervised Relation Extraction Between Suicide-Related Entities and Drugs: Development and Usability Study of an Annotated PubMed Corpus. Journal of Medical Internet Research 2023;25:e41100 View
  2. Bitar H, Babour A, Nafa F, Alzamzami O, Alismail S. Increasing Women’s Knowledge about HPV Using BERT Text Summarization: An Online Randomized Study. International Journal of Environmental Research and Public Health 2022;19(13):8100 View
  3. Kartheeswaran K, Rayan A, Varrieth G. Enhanced disease-disease association with information enriched disease representation. Mathematical Biosciences and Engineering 2023;20(5):8892 View
  4. Huang M, Han J, Lin P, You Y, Tsai R, Hsu W. Surveying biomedical relation extraction: a critical examination of current datasets and the proposal of a new resource. Briefings in Bioinformatics 2024;25(3) View