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Related Concept Videos

Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Protein Complex Assembly

Proteins can form homomeric complexes with another unit of the same protein or heteromeric complexes with different types.  Most protein complexes self-assemble spontaneously via ordered pathways, while some proteins need assembly factors that guide their proper assembly. Despite the crowded intracellular environment, proteins usually interact with their correct partners and form functional complexes.
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Extraction: Advanced Methods

Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is formed in...

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Related Experiment Video

Updated: Jun 12, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Complex event extraction at PubMed scale.

Jari Björne1, Filip Ginter, Sampo Pyysalo

  • 1Department of Information Technology, University of Turku, Turku, Finland. jari.bjorne@utu.fi

Bioinformatics (Oxford, England)
|June 10, 2010
PubMed
Summary
This summary is machine-generated.

Biomedical event extraction systems are now feasible at PubMed scale. This study demonstrates their generalization ability and computational feasibility for large-scale biomedical text mining.

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Area of Science:

  • Biomedical informatics
  • Natural Language Processing
  • Text Mining

Background:

  • Shift from relation to event models in biomedical information extraction (IE).
  • Event models offer detailed representation for complex statements, enabling advanced text mining.
  • Previous evaluations showed potential, but large-scale feasibility and generalization were unstudied.

Purpose of the Study:

  • Evaluate event-based IE at PubMed scale.
  • Assess the generalization performance of event extraction systems.
  • Demonstrate the feasibility of large-scale event extraction from biomedical literature.

Main Methods:

  • Developed a system integrating state-of-the-art domain parsing, named entity recognition, and event extraction.
  • Tested the system on a 1% sample of PubMed citations.
  • Evaluated generalization performance and computational feasibility.

Main Results:

  • Event extraction is computationally feasible at PubMed scale.
  • The developed system demonstrates generalization capabilities.
  • Extracted information provides value for further analysis.

Conclusions:

  • Event extraction systems are viable for large-scale biomedical text mining.
  • The open-source system and data facilitate further research and applications.
  • This work paves the way for advanced text mining applications on the entire PubMed corpus.