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Systems Biology for Multiplatform Data Integration: An Overview.

Elad Ziv1

  • 1Division of General Internal Medicine, Department of Medicine, Helen Diller Family Comprehensive Cancer Center, Institute for Human Genetics, University of California, San Francisco, CA, USA. Elad.Ziv@ucsf.edu.

Methods in Molecular Biology (Clifton, N.J.)
|September 11, 2019
PubMed
Summary
This summary is machine-generated.

This chapter explores multiplatform data integration methods for making inferences across diverse datasets. It highlights joint analyses of genetic variation, gene expression, and phenotypes, particularly for cancer susceptibility.

Keywords:
Cluster analysisModelsPrediction

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genetics

Background:

  • Multiplatform data integration is crucial for comprehensive biological insights.
  • Understanding hereditary aspects of complex diseases requires combining diverse data types.

Purpose of the Study:

  • To examine concepts and methodologies for multiplatform data integration.
  • To illustrate applications in inferring hereditary cancer susceptibility.

Main Methods:

  • Review of inference types possible with integrated data.
  • Discussion of broad methodological considerations.
  • Example of joint analysis of germline genetics, gene expression, and phenotypes.

Main Results:

  • Inferences can be made by integrating diverse data types.
  • Specific methodologies facilitate joint analyses.
  • The approach is exemplified by cancer susceptibility studies.

Conclusions:

  • Multiplatform data integration offers powerful inference capabilities.
  • Concepts discussed apply broadly beyond cancer research.
  • Joint analyses are key for understanding complex traits.