Systems analysis of high-throughput data

Rosemary Braun*

*Corresponding author for this work

Research output: Contribution to journalArticle

17 Scopus citations

Abstract

Modern high-throughput assays yield detailed characterizations of the genomic, transcriptomic, and proteomic states of biological samples, enabling us to probe the molecular mechanisms that regulate hematopoiesis or give rise to hematological disorders. At the same time, the high dimensionality of the data and the complex nature of biological interaction networks present significant analytical challenges in identifying causal variations and modeling the underlying systems biology. In addition to identifying significantly disregulated genes and proteins, integrative analysis approaches that allow the investigation of these single genes within a functional context are required. This chapter presents a survey of current computational approaches for the statistical analysis of high-dimensional data and the development of systems-level models of cellular signaling and regulation. Specifically, we focus on multi-gene analysis methods and the integration of expression data with domain knowledge (such as biological pathways) and other gene-wise information (e.g., sequence or methylation data) to identify novel functional modules in the complex cellular interaction network.

Original languageEnglish (US)
Pages (from-to)153-187
Number of pages35
JournalAdvances in experimental medicine and biology
Volume844
DOIs
StatePublished - 2014

Keywords

  • Genomics
  • High-throughput data
  • Machine learning
  • Microarrays
  • NGS
  • Network models
  • Sequencing
  • Statistical analysis

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)

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