A multiresolution approach to automated classification of protein subcellular location images

Background: 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

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Amina Chebira, Yann Barbotin, Charles Jackson, Thomas E. Merryman, Gowri Srinivasa, Robert F. Murphy, Jelena Kovacevic
• Keywords: BMCBI 2007, Fluorescence Microscopy, Major Subcellular Location, Subcellular Location