Interspecies translation of disease networks increases robustness and predictive accuracy.

ABSTRACT: Gene regulatory networks give important insights into the mechanisms underlying physiology and pathophysiology. The derivation of gene regulatory networks from high-throughput expression data via machine learning strategies is problematic as the reliability of these models is often compromised by limited and highly variable samples, heterogeneity in transcript isoforms, noise, and other artifacts. Here, we develop a novel algorithm, dubbed Dandelion, in which we construct and train intraspecies Bayesian networks that are translated and assessed on independent test sets from other species in a reiterative procedure. The interspecies disease networks are subjected to multi-layers of analysis and evaluation, leading to the identification of the most consistent relationships within the network

SUBMITTER: Anvar SY 

PROVIDER: S-EPMC3207951 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature