Defining functional distance using manifold embeddings of gene ontology annotations

Gilad Lerman, Boris E. Shakhnovich

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


Although rigorous measures of similarity for sequence and structure are now well established, the problem of defining functional relationships has been particularly daunting. Here, we present several manifold embedding techniques to compute distances between Gene Ontology (GO) functional annotations and consequently estimate functional distances between protein domains. To evaluate accuracy, we correlate the functional distance to the well established measures of sequence, structural, and phylogenetic similarities. Finally, we show that manual classification of structures into folds and superfamilies is mirrored by proximity in the newly defined function space. We show how functional distances place structure-function relationships in biological context resulting in insight into divergent and convergent evolution. The methods and results in this paper can be readily generalized and applied to a wide array of biologically relevant investigations, such as accuracy of annotation transference, the relationship between sequence, structure, and function, or coherence of expression modules.

Original languageEnglish (US)
Pages (from-to)11334-11339
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number27
StatePublished - Jul 3 2007


  • Diffusion geometry
  • Domain evolution
  • Functional annotation
  • Homology modeling
  • Kernel methods


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