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Frontiers of Computer Science

, Volume 11, Issue 2, pp 243–252 | Cite as

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

  • Wei Shao
  • Yi Ding
  • Hong-Bin Shen
  • Daoqiang ZhangEmail author
Research Article

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.

Keywords

partial parameter transfer subcellular location classification feature extraction deep model convolution neural network 

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Notes

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).

Supplementary material

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Supplementary material, approximately 516 KB.

References

  1. 1.
    Chou K C, Shen H B. Cell-PLoc: a package ofWeb servers for predicting subcellular localization of proteins in various organisms. Nature protocols, 2008, 3(2): 153–162CrossRefGoogle Scholar
  2. 2.
    Pierleoni A, Martelli P L, Casadio R. MemLoci: predicting subcellular localization of membrane proteins in eukaryotes. Bioinformatics, 2011, 27(9): 1224–1230CrossRefGoogle Scholar
  3. 3.
    Xu Y Y, Yang F, Zhang Y, Shen H B. An image-based multi-label human protein subcellular localization predictor (iLocator) reveals protein mislocalizations in cancer tissues. Bioinformatics, 2013, 29(16): 2032–2040CrossRefGoogle Scholar
  4. 4.
    Hung MC, Link W. Protein localization in disease and therapy. Journal of Cell Science, 2011, 124(20): 3381–3392CrossRefGoogle Scholar
  5. 5.
    Xu Y Y, Yang F, Zhang Y, Shen H B. Bioimaging-based detection of mislocalized proteins in human cancers by semi-supervised learning. Bioinformatics, 2015, 31(7): 1111–1119CrossRefGoogle Scholar
  6. 6.
    Glory E, Newberg J, Murphy R F. Automated comparison of protein subcellular location patterns between images of normal and cancerous tissues. In: Proceedings of the 5th IEEE International Symposium on Biomedical Imaging. 2008Google Scholar
  7. 7.
    Li J, Xiong L, Schneider J, Murphy R F. Protein subcellular location pattern classification in cellular images using latent discriminative models. Bioinformatics. 2012, 28(12): 32–39CrossRefGoogle Scholar
  8. 8.
    Shao W, Liu M, Zhang D. Human cell structure-driven model construction for predicting protein subcellular location from biological images. Bioinformatics, 2016, 32(1): 114–121Google Scholar
  9. 9.
    Galar M, Fernández A, Barrenechea E, Bustince H, Herrera F. An overview of ensemble methods for binary classifiers in multi-class problems: experimental study on one-vs-one and one-vs-all schemes. Pattern Recognition, 2011, 44(8): 1761–1776CrossRefGoogle Scholar
  10. 10.
    Gu B, Sun X, Sheng V S. Structural minimax probability machine. IEEE Transactions on Neural Networks and Learning Systems, 2016, doi:10.1109/TNNLS.2016.2527796Google Scholar
  11. 11.
    Wen X Z, Shao L, Xue Y, Fang W. A rapid learning algorithm for vehicle classification. Information Sciences, 2015, 295(1): 395–406CrossRefGoogle Scholar
  12. 12.
    Glorot X, Bordes A, Bengio Y. Domain adaptation for large-scale sentiment classification: a deep learning approach. In: Proceedings of the 28th International Conference on Machine Learning. 2011Google Scholar
  13. 13.
    Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell, T. Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACMinternational conference on Multimedia. 2014, 675–678Google Scholar
  14. 14.
    Guyon I, Elissee A. An introduction to feature extraction. In: Guyon I, Nikravesh M, Gunn S, et al. eds. Feature Extraction. Studies in Fuzziness and Soft Computing, Vol 207. Springer Berlin Heidelberg, 2006, 1–25Google Scholar
  15. 15.
    Boland M V, Murphy R F. A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells. Bioinformatics, 2001, 17(12): 1213–1223CrossRefGoogle Scholar
  16. 16.
    Tahir M, Khan A. Protein subcellular localization of fluorescence microscopy images: employing new statistical and Texton based image features and SVM based ensemble classification. Information Sciences An International Journal, 2016, 345(C): 65–80CrossRefGoogle Scholar
  17. 17.
    Newberg J, Murphy R F. A framework for the automated analysis of subcellular patterns in human protein atlas images. Journal of Proteome Research, 2008, 7(6): 2300–2308CrossRefGoogle Scholar
  18. 18.
    Nanni L, Lumini A, Brahnam S. Local binary patterns variants as texture descriptors for medical image analysis. Artificial Intelligence in Medicine, 2010, 49(2): 117–125CrossRefGoogle Scholar
  19. 19.
    Yang F, Xu Y Y, Wang S T, Shen H B. Image-based classification of protein subcellular location patterns in human reproductive tissue by ensemble learning global and local features. Neurocomputing, 2014, 131(9): 113–123CrossRefGoogle Scholar
  20. 20.
    Godil A, Lian Z, Wagan A. Exploring local features and the Bag-of-Visual-Words approach for bioimage classification. In: Proceedings of the 17th ACM International Conference on Bioinformatics, Computational Biology and Biomedical Informatics. 2013Google Scholar
  21. 21.
    Coelho L P, Kangas J D, Naik AW, Osuna-Highley E, Glory-Afshar E, Fuhrman M, Simha R, Berget P B, Jarvik J W, Murphy R F. Determining the subcellular location of new proteins from microscope images using local features. Bioinformatics, 2013, 29(18): 2343–2349CrossRefGoogle Scholar
  22. 22.
    Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks. In: Proceedings of Advances in Neural Information Processing Systems. 2012, 1097–1105Google Scholar
  23. 23.
    Sun Q, Amin M, Yan B, Martell C, Markman V, Bhasin A, Ye J. Transfer learning for bilingual content classification. In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2015, 2147–2156CrossRefGoogle Scholar
  24. 24.
    Uhlén M, Ponten F. Antibody-based proteomics for human tissue profiling. Molecular and Cellular Proteomics, 2005, 4(4): 384–393CrossRefGoogle Scholar
  25. 25.
    Uhlén M, Fagerberg L, Hallström B M, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson A, Kampf C, Sjöstedt E, Asplund A, Olsson I, Edlund K, Lundberg E, Navani S, Szigyarto C A K, Odeberg J, Djureinovic D, Takanen J O, Hober S, Alm T, Edqvist P H, Berling H, Tegel H, Mulder J, Rockberg J, Nilsson P, Schwenk J M, Hamsten M, von Feilitzen K, Forsberg M, Persson L, Johansson F, Zwahlen M, von Heijne G, Nielsen J, Pontén F. Tissue-based map of the human proteome. Science, 2015, 347(6220): 1260419CrossRefGoogle Scholar
  26. 26.
    Uhlén M, Oksvold P, Fagerber L, Lundberg E, Jonasson K, Forsberg M, Zwahlen M, Kampf C, Wester K, Hober S, Wernerus H, Björling L, Ponten F. Towards a knowledge-based human protein atlas. Nature Biotechnology, 2010, 28(12): 1248–1250CrossRefGoogle Scholar
  27. 27.
    Wang W, Yang X, Ooi B C, Zhang D, Zhuang Y. Effective deep learning-based multi-modal retrieval. The VLDB Journal, 2016, 25(1): 79–101CrossRefGoogle Scholar
  28. 28.
    Pan Z, Deng Z T. Dimensionality reduction via kernel sparse representation. Frontiers of Computer Science. 2014, 8(5): 807–815MathSciNetCrossRefGoogle Scholar
  29. 29.
    Zhang Y Y, Zhang J C, Pan Z C, Zhang D Q. Multi-view dimensionality reduction via canonical random correlation analysis. Frontiers of Computer Science, 2016, 10(5): 856–869CrossRefGoogle Scholar
  30. 30.
    Tibshirani R. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B (Methodological), 1996, 58(1): 267–288MathSciNetzbMATHGoogle Scholar
  31. 31.
    Magerman D M. Statistical decision-tree models for parsing. In: Proceedings of the 33rd Annual Meeting on Association for Computational Linguistics. 1995, 276–283CrossRefGoogle Scholar
  32. 32.
    Hagan M T, Demuth H B, Beale M H, De Jesús O. Neural Network Design. Boston: PWS Publishing Company, 1996Google Scholar
  33. 33.
    Dietterich T G, Bakiri G. Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 1995, 2(1): 263–286zbMATHGoogle Scholar
  34. 34.
    Escalera S, Tax DMJ, Pujol O, Radeva P, Duin R P. Subclass problemdependent design for error-correcting output codes. IEEE Transactions on Pattern Analysis andMachine Intelligence, 2008, 30(6): 1041–1054CrossRefGoogle Scholar
  35. 35.
    Pujol O, Radeva P, Vitria J. Discriminant ECOC: a heuristic method for application dependent design of error correcting output codes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(6): 1007–1012CrossRefGoogle Scholar
  36. 36.
    Chang C C, Lin C J. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): 27–32CrossRefGoogle Scholar
  37. 37.
    Lin T H, Murphy R F, Bar-Joseph Z. Discriminative motif finding for predicting protein subcellular localization. IEEE/ACMTransactions on Computational Biology and Bioinformatics, 2011, 8(2): 441–451CrossRefGoogle Scholar
  38. 38.
    Zhu L, Yang J, Shen H B. Multi label learning for prediction of human protein subcellular localizations. The Protein Journal, 2009, 28(9): 384–390CrossRefGoogle Scholar
  39. 39.
    Shen H B, Chou K C. A top-down approach to enhance the power of predicting human protein subcellular localization: Hum-mPLoc 2.0. Analytical Biochemistry, 2009, 394(2): 269–274CrossRefGoogle Scholar
  40. 40.
    Zhang D, Wang Y, Zhou L, Yuan H, Shen D, the Alzheimer’s Disease Neuroimaging Initiative. Multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage, 2011, 55(3): 856–867CrossRefGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Wei Shao
    • 1
  • Yi Ding
    • 1
  • Hong-Bin Shen
    • 2
  • Daoqiang Zhang
    • 1
    Email author
  1. 1.School of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.Institute of Image Processing and Pattern RecognitionShanghai Jiao Tong UniversityShanghaiChina

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