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Network-based machine learning and graph theory algorithms for precision oncology

Wei Zhang1, Jeremy Chien2, Jeongsik Yong3

  • 11Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, MN USA.

NPJ Precision Oncology
|June 7, 2018
PubMed

Insights

Network analytics enhances precision oncology by analyzing molecular networks for personalized cancer treatments. This approach identifies tumor mechanisms, drug targets, and repurposed drugs by integrating genomic data with network models.

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Oncology

Background:

  • Precision oncology increasingly utilizes network-based analytics.
  • Understanding cancer through mutated pathways and molecular networks is crucial.
  • Drug efficacy can be predicted using disease modules in molecular networks.

Purpose of the Study:

  • To review network-based machine learning and graph theory algorithms for precision oncology.
  • To facilitate the application of network-based analysis in personalized cancer treatment.
  • To identify tumor-specific molecular mechanisms, drug targets, and repositioned drugs.

Main Methods:

  • Integrative analysis of personal genomic data and biomedical knowledge bases.
  • Review of algorithmic design and mathematical formulation of network-based methods.
  • Application of methods in three scenarios integrating genomic data and network models.
  • Examination of network-based approaches for drug repositioning in drug-disease-gene networks.

Main Results:

  • Comprehensive subnetwork/pathway analysis of mutations from 31 Cancer Genome Atlas projects.
  • Detailed case study on ovarian cancer.
  • Identification of tumor-specific molecular mechanisms and candidate targets.
  • Evaluation of network-based strategies for drug repositioning.

Conclusions:

  • Network-based approaches are vital for advancing precision oncology.
  • These methods enable identification of personalized treatment strategies.
  • Further research is needed to address potential pitfalls and explore future directions.

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