Boosting accuracy of automated classification of fluorescence microscope images for location proteomics

Background: Detailed knowledge of the subcellular location of each expressed protein is critical to a full understanding of its function. Fluorescence microscopy, in combination with methods for fluorescent tagging, is the most suitable current method for proteome-wide determination of subcellular location. Previous work has shown that neural network classifiers can distinguish all major protein subcellular location patterns in both 2D and 3D fluorescence microscope images. Building on these results, we evaluate here new classifiers and features to improve the recognition of protein subcellular location patterns in both 2D and 3D fluorescence microscope images. Results: We report here a thorough comparison of the performance on this problem of eight different state-of-the-art classification methods, including neural networks, support vector machines with linear, polynomial, radial basis, and exponential radial basis kernel functions, and ensemble methods such as AdaBoost, Bagging, and...

• Type: Journal
• Year: 2004
• Where: BMCBI
• Authors: Kai Huang, Robert F. Murphy
• Keywords: BMCBI 2004, Fluorescence Microscope Images, Protein Subcellular Location, Subcellular Location