SLocX: Predicting Subcellular Localization of Arabidopsis Proteins Leveraging Gene Expression Data.

ABSTRACT: Despite the growing volume of experimentally validated knowledge about the subcellular localization of plant proteins, a well performing in silico prediction tool is still a necessity. Existing tools, which employ information derived from protein sequence alone, offer limited accuracy and/or rely on full sequence availability. We explored whether gene expression profiling data can be harnessed to enhance prediction performance. To achieve this, we trained several support vector machines to predict the subcellular localization of Arabidopsis thaliana proteins using sequence derived information, expression behavior, or a combination of these data and compared their predictive performance through a cross-validation test. We show that gene expression carries information about the subcellular l

SUBMITTER: Ryngajllo M 

PROVIDER: S-EPMC3355584 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature