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Deep model-based feature extraction for predicting protein subcellular localizations from bio-images |
Wei SHAO1,Yi DING1,Hong-Bin SHEN2( ),Daoqiang ZHANG1( ) |
1. School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China 2. Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China |
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Abstract Protein subcellular localization prediction is important for studying the function of proteins. Recently, as significant progress has been witnessed in the field of microscopic imaging, automatically determining the subcellular localization of proteins from bio-images is becoming a new research hotspot. One of the central themes in this field is to determine what features are suitable for describing the protein images. Existing feature extraction methods are usually hand-crafted designed, by which only one layer of features will be extracted, which may not be sufficient to represent the complex protein images. To this end, we propose a deep model based descriptor (DMD) to extract the high-level features from protein images. Specifically, in order to make the extracted features more generic, we firstly trained a convolution neural network (i.e., AlexNet) by using a natural image set with millions of labels, and then used the partial parameter transfer strategy to fine-tune the parameters from natural images to protein images. After that, we applied the Lasso model to select the most distinguishing features from the last fully connected layer of the CNN (Convolution Neural Network), and used these selected features for final classifications. Experimental results on a protein image dataset validate the efficacy of our method.
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Keywords
partial parameter transfer
subcellular location classification
feature extraction
deep model
convolution neural network
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Corresponding Author(s):
Hong-Bin SHEN,Daoqiang ZHANG
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Just Accepted Date: 20 January 2017
Online First Date: 23 March 2017
Issue Date: 06 April 2017
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