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

Clinical Information and Decision Making (1436) Decision Support for Health Professionals (1211) Ontologies, Classifications, and Coding (243) Machine Learning (1504) Information Retrieval (296) Infectious Diseases (non-STD/STI) (1592)

Published on in Vol 23, No 6 (2021): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26892, first published .
Constructing High-Fidelity Phenotype Knowledge Graphs for Infectious Diseases With a Fine-Grained Semantic Information Model: Development and Usability Study

Constructing High-Fidelity Phenotype Knowledge Graphs for Infectious Diseases With a Fine-Grained Semantic Information Model: Development and Usability Study

Constructing High-Fidelity Phenotype Knowledge Graphs for Infectious Diseases With a Fine-Grained Semantic Information Model: Development and Usability Study

Authors of this article:

Lizong Deng1, 2 Author Orcid Image ;   Luming Chen1, 2 Author Orcid Image ;   Tao Yang1, 2 Author Orcid Image ;   Mi Liu3 Author Orcid Image ;   Shicheng Li1, 2 Author Orcid Image ;   Taijiao Jiang1, 2, 4 Author Orcid Image
Article Authors Cited by (5) Tweetations (2) Metrics

Lizong Deng   1, 2 * , PhD ;   Luming Chen   1, 2 * , MS ;   Tao Yang   1, 2 * , MM ;   Mi Liu   3 , PhD ;   Shicheng Li   1, 2 , MM ;   Taijiao Jiang   1, 2, 4 , PhD

1 Center of Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China

2 Suzhou Institute of Systems Medicine, Suzhou, China

3 Jiangsu Institute of Clinical Immunology, Jiangsu Key Laboratory of Clinical Immunology, The First Affiliated Hospital of Soochow University, Suzhou, China

4 Guangzhou Laboratory, Guangzhou, China

*these authors contributed equally

Corresponding Author:

  • Taijiao Jiang, PhD
  • Center of Systems Medicine
  • Institute of Basic Medical Sciences
  • Chinese Academy of Medical Sciences & Peking Union Medical College
  • #5 Dong Dan San Tiao
  • Dongcheng District
  • Beijing, 100005
  • China
  • Phone: 86 051262873781
  • Email: taijiao@ibms.pumc.edu.cn