The growing availability of large-scale functional networks has promoted the development of many successful techniques for predicting functions of genes. Here we extend these network-based principles and techniques to functionally characterize whole sets of genes. We present RIDDLE (Reflective Diffusion and Local Extension), which uses well developed guilt-by-association principles upon a human gene network to identify associations of gene sets. RIDDLE is particularly adept at characterizing sets with no annotations, a major challenge where most traditional set analyses fail. Notably, RIDDLE found microRNA-450a to be strongly implicated in ocular diseases and development. A web application is available at http://www.functionalnet.org/RIDDLE.
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (2010-0017649, 2012-0001179) and the Next-Generation BioGreen 21 Program (SSAC, PJ009029) Rural Development Administration of Korea to IL, from the NSF, NIH, US Army Research (58343-MA) and Welch (F1515) and Packard Foundations to EMM, and the NIH (RO1AI077746) to CSS.
All Science Journal Classification (ASJC) codes
- Ecology, Evolution, Behavior and Systematics
- Cell Biology