Integrating functional genomics data

Insuk Lee, Edward M. Marcotte

Research output: Chapter in Book/Report/Conference proceedingChapter

9 Citations (Scopus)


The revolution in high throughput biology experiments producing genome-scale data has heightened the challenge of integrating functional genomics data. Data integration is essential for making reliable inferences from functional genomics data, as the datasets are neither error-free nor comprehensive. However, there are two major hurdles in data integration: heterogeneity and correlation of the data to be integrated. These problems can be circumvented by quantitative testing of all data in the same unified scoring scheme, and by using integration methods appropriate for handling correlated data. This chapter describes such a functional genomics data integration method designed to estimate the "functional coupling" between genes, applied to the baker's yeast Saccharomyces cerevisiae. The integrated dataset outperforms individual functional genomics datasets in both accuracy and coverage, leading to more reliable and comprehensive predictions of gene function. The approach is easily applied to multicellular organisms, including human.

Original languageEnglish
Title of host publicationBioinformatics
Subtitle of host publicationStructure, Function and Applications
PublisherHumana Press
Number of pages12
ISBN (Print)9781603274289
Publication statusPublished - 2008

Publication series

NameMethods in Molecular Biology
ISSN (Print)1064-3745

All Science Journal Classification (ASJC) codes

  • Molecular Biology
  • Genetics


Dive into the research topics of 'Integrating functional genomics data'. Together they form a unique fingerprint.

Cite this