Hypothesis Generation from Heterogeneous Datasets

Yves A. Lussier, Haiquan Li

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations


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.
Number of pages18
ISBN (Print)9780124016781
StatePublished - Oct 2013


  • 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

  • General Biochemistry, Genetics and Molecular Biology


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