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Robust pathway-based multi-omics data integration using directed random walks for survival prediction in multiple

So Yeon Kim1, Hyun-Hwan Jeong2,3, Jaesik Kim1

  • 1Department of Computer Engineering, Ajou University, Suwon, 16499, South Korea.

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|May 1, 2019
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Summary

This study introduces an integrative directed random walk (iDRW) method for multi-omics data integration in cancer research. The iDRW method improves survival prediction and identifies cancer-related pathways and genes using gene expression and copy number data.

Keywords:
Breast cancerIntegrative analysisMulti-omicsNeuroblastomaPathway-based analysisRandom walk

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Multi-omics data integration is crucial for cancer survival prediction and biomarker discovery.
  • Pathway information aids in analyzing multiple genomic profiles more effectively than individual profiles.

Purpose of the Study:

  • To apply and evaluate the integrative directed random walk (iDRW) method for multi-omics data integration.
  • To improve survival prediction and biological insights in cancer datasets.

Main Methods:

  • Applied the iDRW method to gene expression and copy number data for neuroblastoma and breast cancer.
  • Designed a directed gene-gene graph incorporating gene expression and copy number interactions.
  • Compared iDRW performance against four state-of-the-art pathway-based methods using a survival prediction model.

Main Results:

  • The iDRW method enhanced prediction performance in both cancer datasets.
  • Integrative analysis using pathway information provided better biological insights.
  • Prioritized pathways and genes were found to be relevant to neuroblastoma and breast cancer.

Conclusions:

  • The iDRW method effectively integrates multi-omics data for cancer survival prediction.
  • The revamped iDRW approach improved performance and identified cancer-specific pathways and genes.
  • Demonstrated the utility of iDRW for gene expression and copy number data integration in cancer research.