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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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A framework for exploring associations between biomedical terms in PubMed.

Haixiu Yang1, Lingling Zhao2, Ying Zhang3

  • 1College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China.

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|December 22, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a text mining framework to identify biomedical term associations using PubMed co-occurrence data. The approach effectively links Gene Ontology and Disease Ontology terms, aiding in disease network construction.

Keywords:
co-occurrence relationshipframeworkterm associationtext mining

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Text Mining

Background:

  • Manually curated relationships in biomedical vocabularies are limited.
  • The rapid growth of biomedical literature necessitates automated methods for identifying term associations.
  • Co-occurrence relationships offer a valuable approach to discovering these associations.

Purpose of the Study:

  • To propose and validate a framework for exploring term associations using text mining and co-occurrence analysis.
  • To investigate the semantic relationships between Gene Ontology (GO) and Disease Ontology (DO) terms.
  • To construct a disease association network (DAN) based on identified co-occurrence relationships.

Main Methods:

  • PubMed literature was segmented into sentences using Apache OpenNLP.
  • Terms within sentences were identified using MGREP.
  • Co-occurrence relationships were established for terms within the same sentence.
  • Relationship degrees were calculated using Normalized MEDLINE Distance (NMD) and relationship-scaled score (RSS).

Main Results:

  • The framework successfully identified associations between GO and DO terms based on co-occurrence.
  • Increased co-occurrence between term pairs correlated with shared semantic relationships in ontologies and genes.
  • The constructed disease association network demonstrated that diseases within the same class exhibit stronger linkages.

Conclusions:

  • The proposed framework effectively leverages co-occurrence relationships for discovering biomedical term associations.
  • This method aids in understanding semantic links between biological entities and diseases.
  • The findings support the utility of co-occurrence analysis in building biologically relevant networks.