High Throughput Location Proteomics in Confocal Images from the Human Protein Atlas Using a Bag-of-Features Representation

Abstract

This work addresses the problem of predicting protein subcellular locations within cells from confocal images, which is a key issue to reveal information about cell function. The Human Protein Atlas (HPA) is a world-scale project addressed at proteomics research. The HPA stores immunohistological and immunofluorescence images from most human tissues. This paper concentrates on the problem of analyzing HPA immunofluorescence images from immunohistochemically stained tissues and cells to automatically identify the subcellular location of particular proteins expression. This problem has been previously tackled using computer vision methods which train classification models able to discriminate subcellular locations based on particular visual features extracted form images. One of the challenges of applying this approach is the high computational cost of training the computer vision models, which includes the cost of visual feature extraction from multichannel images, classifier training and evaluation, and parameter tuning. This work addresses this challenging problem using a high-throughput computer-vision approach by (1) learning a visual dictionary of the image collection for representing visual content through a bag-of-features histogram image representation, (2) using supervised learning process to predict subcellular locations of proteins and (3) developing a software framework to seamlessly develop machine learning algorithms for computer vision and harness computing power for those processes.