Abstract
Effective and successful clinical trials are essential in developing new drugs and advancing new treatments. However, clinical trials are very expensive and easy to fail. The high cost and low success rate of clinical trials motivate research on inferring knowledge from existing clinical trials in innovative ways for designing future clinical trials. In this manuscript, we present our efforts on constructing the first publicly available Clinical Trials Knowledge Graph, denoted as CTKG. CTKG includes nodes representing medical entities in clinical trials (e.g., studies, drugs and conditions), and edges representing the relations among these entities (e.g., drugs used in studies). Our embedding analysis demonstrates the potential utilities of CTKG in various applications such as drug repurposing and similarity search, among others.
| Original language | English (US) |
|---|---|
| Article number | 4724 |
| Journal | Scientific reports |
| Volume | 12 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 2022 |
| Externally published | Yes |
Bibliographical note
Funding Information:This project was made possible, in part, by support from the National Science Foundation under Grant Number IIS-1855501. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies.
Publisher Copyright:
© 2022, The Author(s).
PubMed: MeSH publication types
- Journal Article
- Research Support, U.S. Gov't, Non-P.H.S.