Ontology highlight
ABSTRACT: Motivation
Gene expression assays allow for genome scale analyses of molecular biological mechanisms. State-of-the-art data analysis provides lists of involved genes, either by calculating significance levels of mRNA abundance or by Bayesian assessments of gene activity. A common problem of such approaches is the difficulty of interpreting the biological implication of the resulting gene lists. This lead to an increased interest in methods for inferring high-level biological information. A common approach for representing high level information is by inferring gene ontology (GO) terms which may be attributed to the expression data experiment.Results
This article proposes a probabilistic model for GO term inference. Modelling assumes that gene annotations to GO terms are ava
SUBMITTER: Sykacek P
PROVIDER: S-EPMC3436832 | biostudies-literature | 2012 Sep
REPOSITORIES: biostudies-literature