Protein networks markedly improve prediction of subcellular localization in multiple eukaryotic species.

ABSTRACT: The function of a protein is intimately tied to its subcellular localization. Although localizations have been measured for many yeast proteins through systematic GFP fusions, similar studies in other branches of life are still forthcoming. In the interim, various machine-learning methods have been proposed to predict localization using physical characteristics of a protein, such as amino acid content, hydrophobicity, side-chain mass and domain composition. However, there has been comparatively little work on predicting localization using protein networks. Here, we predict protein localizations by integrating an extensive set of protein physical characteristics over a protein's extended protein-protein interaction neighborhood, using a classification framework called 'Divide and Conquer k-

SUBMITTER: Lee K 

PROVIDER: S-EPMC2582614 | biostudies-literature | 2008 Nov

REPOSITORIES: biostudies-literature