Fluorescence microscopy is widely used to determine the subcellular location of proteins. Efforts to determine location on a proteome-wide basis create a need for automated methods to analyze the resulting images. Over the past ten years, the feasibility of using machine learning methods to recognize all major subcellular location patterns has been convincingly demonstrated, using diverse feature sets and classifiers. On a well-studied data set of 2D HeLa single-cell images, the best performance to date, 91.5%, was obtained by including a set of multiresolution features. This demonstrates the value of multiresolution approaches to this important problem.
['Amina Chebira', 'Yann Barbotin', 'Charles Jackson', 'Thomas E. Merryman', 'Gowri Srinivasa', 'Robert F. Murphy', 'Jelena Kovacevic']
A multiresolution approach to automated classification of protein subcellular location images