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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 semiautomated framework for integrating expert knowledge into disease marker identification.

Jing Wang1, Bobbie-Jo M Webb-Robertson, Melissa M Matzke

  • 1Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, WA 99352, USA.

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Summary

This study introduces a novel framework, ISIC, for integrating expert knowledge into biomarker discovery from large datasets. This approach enhances the robustness of identified disease biomarkers, improving diagnostic accuracy.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Proteomics

Background:

  • High-throughput technologies generate large datasets, facilitating disease biomarker studies.
  • Integrating expert knowledge into biomarker selection from complex data remains a challenge.

Purpose of the Study:

  • Develop a generalizable framework for semi-automated integration of expert knowledge into data-driven biomarker selection.
  • Provide an optimization metric for biomarker selection schemes.

Main Methods:

  • Implemented a five-component pipeline for Identification by Signatures from Integrated Clustering (ISIC).
  • Combined distance-based clustering with expert knowledge-driven functional selection for biomarker identification.

Main Results:

  • Demonstrated ISIC utility on proteomics data for chronic obstructive pulmonary disease (COPD).
  • Identified and validated biomarker candidates in mouse models and human cohorts.

Conclusions:

  • Expert knowledge integration enhances the robustness of biomarker candidates.
  • Strategies for extracting orthogonal and robust features improve biomarker discovery success.