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e-TSN: an interactive visual exploration platform for target-disease knowledge mapping from literature.

Ziyan Feng1, Zihao Shen1, Honglin Li1,2,3

  • 1Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.

Briefings in Bioinformatics
|November 8, 2022
PubMed
Summary

e-TSN is a novel web server that visualizes target-disease networks from biomedical literature. It aids in prioritizing drug targets by analyzing associations and drug-target relationships, accelerating drug discovery.

Keywords:
knowledge graphstarget discoverytext miningvisualization

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Drug Discovery

Background:

  • Biomedical data expansion necessitates advanced tools for target discovery.
  • Text mining biomedical literature is crucial but faces data lag issues.
  • Knowledge graphs can integrate heterogeneous biomedical data.

Purpose of the Study:

  • To introduce e-TSN, a web server for visualizing target-disease associations.
  • To facilitate the prioritization of candidate disease-related proteins.
  • To aid in understanding disease mechanisms and improving drug development efficiency.

Main Methods:

  • Developed e-TSN, a knowledge visualization web server.
  • Integrated a large database of target-disease associations from scientific literature.
  • Implemented significance and novelty scoring based on bibliometric statistics.
  • Included approved drugs and bioactivities for target analysis.

Main Results:

  • e-TSN visualizes target-disease knowledge graphs.
  • The platform provides significance and novelty scores for targets.
  • Drug-target relationships and bioactivities are visualized.
  • e-TSN aids in analyzing complex target-disease networks.

Conclusions:

  • e-TSN is a fast, customizable resource for drug target exploration.
  • The tool enhances understanding of disease mechanisms.
  • e-TSN can improve drug discovery and development efficiency, particularly during pandemics like COVID-19.