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A Web Tool for Generating High Quality Machine-readable Biological Pathways
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BioTAGME: A Comprehensive Platform for Biological Knowledge Network Analysis.

Antonio Di Maria1, Salvatore Alaimo1, Lorenzo Bellomo2

  • 1Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy.

Frontiers in Genetics
|May 16, 2022
PubMed
Summary
This summary is machine-generated.

BioTAGME integrates entity annotation and network inference to build a biomedical knowledge graph from PubMed literature, enabling new scientific discoveries.

Keywords:
DT-hybridTAGMEannotation toolsknowledge graphtext miningwikipedia

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Text Mining

Background:

  • Scientific discovery relies on inferring new knowledge from existing literature.
  • The vastness of biomedical literature presents challenges for automated knowledge extraction.
  • Identifying relationships between biological entities is crucial for advancing research.

Purpose of the Study:

  • To develop BioTAGME, a system for automated knowledge extraction from biomedical literature.
  • To construct a comprehensive Knowledge Graph of biological terms and their relationships.
  • To facilitate the discovery of novel hypotheses and scientific insights.

Main Methods:

  • Utilized the TAGME tool for entity annotation using a Wikipedia corpus.
  • Employed a network-based inference methodology (DT-Hybrid).
  • Integrated these components into a scalable framework using Scala, Spark, Laravel, and Neo4j.

Main Results:

  • Created an extensive Knowledge Graph from PubMed titles and abstracts.
  • Modeled semantic relationships among biological terms and phrases.
  • Developed a robust system with a distributed back-end and a user-friendly front-end.

Conclusions:

  • BioTAGME effectively extracts and models relationships from biomedical literature.
  • The system supports the generation of new scientific knowledge and hypotheses.
  • Enables advanced analysis of biomedical information through a structured Knowledge Graph.