Protein localization on cellular images with Markov random fields

There has been an increasing interest recently in identifying subcellular proteins from cellular images in order to understachind subcellular activities of cells. However, accuracies of the prediction tend to decrease with the number of protein subcellular localization classes. Therefore in this paper, we introduce a multiple-cell model with a higher-order Markov random fields (MRF) to combine predictions on multiple cells to make inferences on protein localizations of individual cells. The proposed method showed a significant improvement in discrimination of protein subcellular localization patterns over the predictions by single cells. We also introduce structure learning of MRF, which indeed enhanced the predictions especially when the number of cells in the model becomes large.