Hypothesis Generation from Heterogeneous Datasets

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The last decade has seen a rise in the rapid accumulation of large-scale data from both genomic technologies and from increased use of electronic health records. These advances have been accompanied by opportunities for automatic hypothesis generation in translational research; however, integrating and mining these highly heterogeneous datasets remains challenging. This chapter addresses the major principles and methods that are associated with providing effective solutions to a broad range of these problems. Indeed, these principles include issues of representation, biological scales of measurements, feature selection, and statistical approaches to address the curse of dimensionality, and approaches of integration that we divide into corroborative versus fusion approaches.

Original languageEnglish (US)
Title of host publicationMethods in Biomedical Informatics
Subtitle of host publicationA Pragmatic Approach
PublisherElsevier Inc.
Pages81-98
Number of pages18
ISBN (Print)9780124016781
DOIs
StatePublished - Oct 1 2013
Externally publishedYes

Keywords

  • Biomarkers
  • Complex diseases
  • Corroborative mining
  • Data fusion
  • Disease genes
  • Disease modules
  • Genome-wide association studies
  • Heterogeneous data Sources
  • Hypothesis generation
  • Knowledge discovery
  • SNP

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)

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  • Cite this

    Lussier, Y. A., & Li, H. (2013). Hypothesis Generation from Heterogeneous Datasets. In Methods in Biomedical Informatics: A Pragmatic Approach (pp. 81-98). Elsevier Inc.. https://doi.org/10.1016/B978-0-12-401678-1.00004-X