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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Gene Ontology density estimation and discourse analysis for automatic GeneRiF extraction.

Julien Gobeill1, Imad Tbahriti, Frédéric Ehrler

  • 1University and Hospitals of Geneva, Geneva, Switzerland.

BMC Bioinformatics
|May 9, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a novel engine for extracting Gene Reference into Functions (GeneRiF) from MEDLINE records. Combining argumentative and Gene Ontology features significantly improves functional annotation accuracy in proteomics.

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Last Updated: Jul 5, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Describes a sentence selection engine for extracting Gene Reference into Functions (GeneRiF) from MEDLINE records.
  • Inputs include gene information and MEDLINE references.
  • Aims to automate the extraction of functional annotations for genes.

Purpose of the Study:

  • To develop and evaluate a sentence selection engine for GeneRiF extraction.
  • To merge two independent sentence extraction strategies for improved performance.
  • To enhance functional annotation in proteomics through automated information retrieval.

Main Methods:

  • Employs a combined approach merging two sentence extraction strategies: LASt (argumentative features) and GOEx (Gene Ontology category density).
  • LASt utilizes discourse-analysis models for feature extraction.
  • GOEx uses text categorization to rank sentences based on Gene Ontology content density.
  • Filters non-content bearing phrases to refine the selected segment.

Main Results:

  • The LASt strategy achieved a competitive score of 52.78% on the TREC-2003 Genomics collection.
  • The combined approach demonstrated significant improvement, achieving a Dice score over 57% (a 10% increase).
  • The engine successfully extracts GeneRiFs, enhancing functional annotation capabilities.

Conclusions:

  • Argumentative representation levels and Gene Ontology conceptual density are complementary for functional annotation.
  • The proposed combined approach offers a more effective method for GeneRiF extraction.
  • This work contributes to advancing automated functional annotation in proteomics research.