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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Identifying dynamic pathway interactions based on clinical information.

Shinuk Kim1

  • 1Department of Civil Engineering, Sangmyung University, Cheonan Chungnam 31066, Republic of Korea.

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|May 3, 2017
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Summary
This summary is machine-generated.

This study converts static cancer data into dynamic pathway interactions using patient clinical information. It identifies significant pathway pairs, including CELL ADHESION MOLECULES and SYSTEMIC LUPUS ERYTHEMATOSUS, offering new insights into cancer biology.

Keywords:
Clinical informationDynamic pathwayPathway interaction

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Static biological datasets limit understanding of dynamic pathway interactions.
  • Integrating patient clinical information can enhance pathway analysis.

Purpose of the Study:

  • To develop and evaluate methods for inferring dynamic pathway interactions from static datasets.
  • To identify significant pathway pairs and common genes using clinical data.

Main Methods:

  • Conversion of static datasets to dynamic datasets using patient survival time, grade, and stage.
  • Generation of six dynamic levels based on cancer characteristics.
  • Enrichment analysis of twelve pathways to identify significant pairs.

Main Results:

  • Two significant pathway pairs were identified from twelve enriched pathways.
  • CELL ADHESION MOLECULES (CAMS) and SYSTEMIC LUPUS ERYTHEMATOSUS showed a correlation of 1.00, with common genes CD28, CD86, HLA-DOA, and HLA-DOB.
  • SPLICEOSOME and PRIMARY IMMUNODEFICIENCY showed a correlation of 0.94, with no common genes identified.

Conclusions:

  • Dynamic pathway inference using clinical data is feasible and can reveal novel interactions.
  • The identified pathway pairs and genes provide potential targets for further investigation in cancer research.