Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院
Journal:FRONTIERS OF COMPUTER SCIENCE
Key Words:partial parameter transfer subcellular location classification feature extraction deep model convolution neural network
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.
ISSN No.:2095-2228
Translation or Not:no
Date of Publication:2017-04-01
Co-author:dy,Shen, Hong-Bin,Zhang Daoqiang
Correspondence Author:gsw