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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Multidimensional knowledge-based framework is an essential step in the categorization of gene sets in complex

A Sreeja1, K P Vinayan2

  • 1* Department of Computer Science & IT, School of Arts and Sciences, Amrita University, Kochi, Kerala, India.

Journal of Bioinformatics and Computational Biology
|November 9, 2017
PubMed
Summary

This study introduces a novel gene prioritization method for complex disorders by integrating multiple data sources. This approach improves accuracy and aids in understanding genetic links to conditions like Autism Spectrum Disorder.

Keywords:
MeSH termsendophenotypegene categorizationgene ontologymolecular function

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Complex disorders involve multiple genes, necessitating advanced methods for gene discovery.
  • Current gene prioritization techniques often rely on single data sources or simple combinations, limiting comprehensive analysis.

Purpose of the Study:

  • To develop a flexible, multi-source data reconciliation approach for accurate gene prioritization.
  • To enhance the discovery of molecular mechanisms underlying complex genetic disorders.

Main Methods:

  • Proposed a novel integrative strategy for multi-source data reconciliation in gene prioritization.
  • Applied and validated the framework using Autism Spectrum Disorder (ASD) as a case study.
  • Compared integrated rankings against individual source rankings.

Main Results:

  • The integrated approach significantly reduced false positives and improved gene prioritization performance compared to single sources.
  • Clinical phenotype validation for ASD revealed strong associations between top-ranked genes and disorder endophenotypes.
  • The method demonstrated potential for identifying genes linked to complex neurological and psychiatric disorders.

Conclusions:

  • The proposed flexible approach effectively integrates heterogeneous data for superior gene prioritization.
  • This method facilitates hypothesis generation for clinical and translational research in complex genetic disorders.
  • The framework is adaptable for studying other complex neurological and psychiatric conditions.