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Deep model-based feature extraction for predicting protein subcellular localizations from bio-images

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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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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant Nos. 61422204, 61473149 and 61671288), Jiangsu Natural Science Foundation for Distinguished Young Scholar (BK20130034), and Science and Technology Commission of Shanghai Municipality (16JC1404300).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Wei Shao, Yi Ding & Daoqiang Zhang

  2. Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China

    Hong-Bin Shen

Authors
  1. Wei Shao
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  2. Yi Ding
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  3. Hong-Bin Shen
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  4. Daoqiang Zhang
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Corresponding author

Correspondence to Daoqiang Zhang.

Additional information

Wei Shao received the BS and MS degrees from Nanjng University of Technology, China in 2009 and 2012, respectively. He is current working torward the PhD degree in computer science from Nanjing University of Aeronautics and Astronautics, China. His current research interest is bioinformatics.

Yi Ding received the BS degree in information security from Nanjing University of Aeronautics and Astronautics (NUAA), China in 2015. In the same year, he was admitted to study for MS degree at NUAA without entrance examination. He is currently a member of the PARNEC Group led by Songcan Chen and joined iBrain Group led by Daoqiang Zhang. His research interests mainly include machine learning and data mining.

Hong-Bin Shen received his PhD degree from Shanghai Jiao Tong University (SJTU), China in 2007. He was a postdoctoral research fellow of Harvard Medical School from 2007 to 2008, and a visiting professor of University of Michigan, USA in 2012. Currently, he is a distinguished professor of Institute of Image Processing and Pattern Recognition, SJTU. His research interests include pattern recognition and bioinformatics. He has published more than 100 journal papers and constructed 35 bioinformatics severs in these areas. He is the 2014 and 2015 ESI highly cited researcher. He serves as the associate editor of BMC Bioinformatics and the editorial board members of several international journals.

Daoqiang Zhang received the BS degree and PhD degree in computer science from Nanjing University of Aeronautics and Astronautics (NUAA), China in 1999 and 2004, respectively. He joined the Department of Computer Science and Engineering of NUAA as a lecturer in 2004, and is a professor at present. His research interests include machine learning, pattern recognition, data mining, and medical image analysis. In these areas, he has published over 100 scientific articles in refereed international journals such as Neuroimage, Pattern Recognition, Artificial Intelligence in Medicine, IEEE Trans. Neural Networks; and conference proceedings such as IJCAI, AAAI, SDM, ICDM.

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Shao, W., Ding, Y., Shen, HB. et al. Deep model-based feature extraction for predicting protein subcellular localizations from bio-images. Front. Comput. Sci. 11, 243–252 (2017). https://doi.org/10.1007/s11704-017-6538-2

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  • DOI: https://doi.org/10.1007/s11704-017-6538-2

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