C Jackson1, E Glory-Afshar, R F Murphy, J Kovacevic. 1. Center for Bioimage Informatics, Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA.
Abstract
MOTIVATION: We present a framework and algorithms to intelligently acquire movies of protein subcellular location patterns by learning their models as they are being acquired, and simultaneously determining how many cells to acquire as well as how many frames to acquire per cell. This is motivated by the desire to minimize acquisition time and photobleaching, given the need to build such models for all proteins, in all cell types, under all conditions. Our key innovation is to build models during acquisition rather than as a post-processing step, thus allowing us to intelligently and automatically adapt the acquisition process given the model acquired. RESULTS: We validate our framework on protein subcellular location classification, and show that the combination of model building and intelligent acquisition results in time and storage savings without loss of classification accuracy, or alternatively, higher classification accuracy for the same total acquisition time. AVAILABILITY AND IMPLEMENTATION: The data and software used for this study will be made available upon publication at http://murphylab.web.cmu.edu/software and http://www.andrew.cmu.edu/user/jelenak/Software. CONTACT: jelenak@cmu.edu.
MOTIVATION: We present a framework and algorithms to intelligently acquire movies of protein subcellular location patterns by learning their models as they are being acquired, and simultaneously determining how many cells to acquire as well as how many frames to acquire per cell. This is motivated by the desire to minimize acquisition time and photobleaching, given the need to build such models for all proteins, in all cell types, under all conditions. Our key innovation is to build models during acquisition rather than as a post-processing step, thus allowing us to intelligently and automatically adapt the acquisition process given the model acquired. RESULTS: We validate our framework on protein subcellular location classification, and show that the combination of model building and intelligent acquisition results in time and storage savings without loss of classification accuracy, or alternatively, higher classification accuracy for the same total acquisition time. AVAILABILITY AND IMPLEMENTATION: The data and software used for this study will be made available upon publication at http://murphylab.web.cmu.edu/software and http://www.andrew.cmu.edu/user/jelenak/Software. CONTACT: jelenak@cmu.edu.
Authors: Kevin W Eliceiri; Michael R Berthold; Ilya G Goldberg; Luis Ibáñez; B S Manjunath; Maryann E Martone; Robert F Murphy; Hanchuan Peng; Anne L Plant; Badrinath Roysam; Nico Stuurman; Nico Stuurmann; Jason R Swedlow; Pavel Tomancak; Anne E Carpenter Journal: Nat Methods Date: 2012-06-28 Impact factor: 28.547
Authors: Marlies Verschuuren; Jonas De Vylder; Hannes Catrysse; Joke Robijns; Wilfried Philips; Winnok H De Vos Journal: PLoS One Date: 2017-01-26 Impact factor: 3.240