Subcellular protein distribution software tools | Bright-field microscopy
Human cells are organized into compartments of different biochemical cellular processes. Having proteins appear at the right time to the correct locations in the cellular compartments is required to conduct their functions in normal cells, whereas mislocalization of proteins can result in pathological diseases, including cancer.
To reveal the cancer-related protein mislocalizations, an image-based multi-label subcellular location predictor which covers seven cellular localizations was developed. The iLocator incorporates both global and local image descriptors and generates predictions by using an ensemble multi-label classifier. The algorithm has the ability to treat both single- and multiple-location proteins.
A semi-supervised protocol that can use unlabeled cancer protein data in model construction by an iterative and incremental training strategy. Experiments demonstrate that the new semi-supervised protocol can result in improved accuracy and sensitivity of subcellular location difference detection.