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Open AccessPreprint10.5220/0003821905790584

A BIO-INSPIRED LEARNING AND CLASSIFICATION METHOD FOR SUBCELLULAR LOCALIZATION OF A PLASMA MEMBRANE PROTEIN

Wafa Bel Haj Ali,Paolo Piro,Lydie Crescence,Dario Giampaglia,Oumelkheir Ferhat,Jacques Darcourt+2 more-2012-01-01
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Abstract: High-content cellular imaging is an emerging technology for studying many biological phenomena. statistical analyses on large populations (more than thousands) of cells are required. Hence classifying cells by experts is a very time-consuming task and poorly reproducible. In order to overcome such limitations, we propose an automatic supervised classification method. Our new cell classification method consists of two steps: The first one is an indexing process based on specific bio-inspired features using contrast information distributions on cell sub-regions. The second is a supervised learning process to select prototypical samples (that best represent the cells categories) which are used in a leveraged k-NN framework to predict the class of unlabeled cells. In this paper we have tested our new learning algorithm on cellular images acquired for the analysis of changes in the subcellular localization of a membrane protein (the sodium iodide symporter). In order to evaluate t

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Abstract: High-content cellular imaging is an emerging technology for studying many biological phenomena. statistical analyses on large populations (more than thousands) of cells are required. Hence classifying cells by experts is a very time-consuming task and poorly reproducible. In order to overcome such limitations, we propose an automatic supervised classification method. Our new cell classification method consists of two steps: The first one is an indexing process based on specific bio-inspired features using contrast information distributions on cell sub-regions. The second is a supervised learning process to select prototypical samples (that best represent the cells categories) which are used in a leveraged k-NN framework to predict the class of unlabeled cells. In this paper we have tested our new learning algorithm on cellular images acquired for the analysis of changes in the subcellular localization of a membrane protein (the sodium iodide symporter). In order to evaluate t

Keywords

Artificial intelligenceComputer scienceMachine learningPattern recognition (psychology)Process (computing)Supervised learningArtificial neural network

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