Incorporating organelle correlations into semi-supervised learning for protein subcellular localization prediction

Bioimages of subcellular protein distribution as a new data source have attracted much attention in the field of automated prediction of proteins subcellular localization. Performance of existing systems is significantly limited by the small number of high-quality images with explicit annotations, resulting in the small sample size learning problem. This limitation is more serious for the multi-location proteins that co-exist at two or more organelles, because it is difficult to accurately annotate those proteins by biological experiments or automated systems.