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Relation extraction for biological pathway construction using node2vec.

Munui Kim1, Seung Han Baek1, Min Song2

  • 1Department of Library and Information Science, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul, Republic of Korea.

BMC Bioinformatics
|June 14, 2018
PubMed
Summary
This summary is machine-generated.

Node2vec effectively extracts gene relationships from biomedical texts, outperforming other methods for type 2 diabetes pathway analysis. This approach aids in automatic biological pathway construction.

Keywords:
Biological pathwaysPathway extractionRelation extraction

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Area of Science:

  • Systems biology
  • Bioinformatics
  • Computational biology

Background:

  • Systems biology analyzes biological mechanisms through component interactions.
  • Biological pathways are crucial for understanding complex biological systems.
  • Extracting pathway interactions from numerous biomedical reports requires automated text-mining.

Purpose of the Study:

  • To apply node2vec for automated relationship extraction in biological pathways.
  • To evaluate node2vec's performance in identifying gene-gene interactions within a specific pathway.

Main Methods:

  • Genes were extracted from abstracts using the pkde4j text-mining tool.
  • A gene co-occurrence network was built from extracted genes.
  • Node2vec was employed for feature learning and generating latent representations within the network.

Main Results:

  • Node2vec demonstrated superior performance in detecting gene relationships within the type 2 diabetes pathway.
  • The method effectively captured relatedness between biological entities in pathways.
  • Node2vec's efficacy was compared against baseline methods like co-occurrence and DeepWalk.

Conclusions:

  • Node2vec is a suitable method for automated biological pathway construction.
  • The algorithm accurately identifies relationships between biological entities.
  • This approach enhances the understanding of complex biological mechanisms.