Image-Based Human Protein Subcellular Location Prediction Using Local Tetra Patterns Descriptor

  • Fan YangEmail author
  • Yang Liu
  • Han Wei
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 905)


Protein subcellular location has a huge positive influence on understanding protein function. In the past decades, many image-based automated approaches have been published for predicting protein subcellular location. However, in the reported literatures, there is a common deficiency for diverse prediction models in capturing local information of interest region of image. It motivates us to propose a novel approach by employing local feature descriptor named the Local Tetra Patterns (LTrP). In this paper, local features together with global features were fed to support vector machine to train chain classifiers, which can deal with multi-label datasets by using problem transformation pattern. To verify the validity of our approach, three different experiments were conducted based on the same benchmark dataset. The results show that the performance of the classification with LTrP descriptor not only captured more local information in interest region of images but also contributed to the improvement of prediction precision since the local descriptor is encoded along horizontal and vertical directions by LTrP. By applying the new approach, a more accurate classifier of protein subcellular location can be modeled, which is crucial to screen cancer biomarkers and research pathology mechanisms.


Human protein Subcellular location prediction Local tetra patterns Multi-label learning 



This study was supported by the National Natural Science Foundation of China (61603161), and the Science Foundation of Artificial Intelligence and Bioinformatics Cognitive Research Base Fund of Jiangxi Science and Technology Normal University of China (2017ZDPYJD005) and the Key Science Foundation of Educational Commission of Jiangxi Province of China (GJJ160768).


  1. 1.
    Shao, W., Ding, Y., Shen, H.B., Zhang, D.: Deep model-based feature extraction for predicting protein subcellular localizations from bio-images. Front. Comput. Sci. 11(2), 243–252 (2017)CrossRefGoogle Scholar
  2. 2.
    Xu, Y.Y., Yang, F., Shen, H.B.: Incorporating organelle correlations into semi-supervised learning for protein subcellular localization prediction. Bioinformatics 32(14), 2184 (2016)CrossRefGoogle Scholar
  3. 3.
    Zhou, H., Yang, Y., Shen, H.B.: Hum-mPLoc 3.0: prediction enhancement of human protein subcellular localization through modeling the hidden correlations of gene ontology and functional domain features. Bioinformatics 33(6), 843 (2016)Google Scholar
  4. 4.
    Zhao, T., Velliste, M., Boland, M.V., Murphy, R.F.: Object type recognition for automated analysis of protein subcellular location. IEEE Trans. Image Process. 14(9), 1351–1359 (2005)CrossRefGoogle Scholar
  5. 5.
    Peng, T., Vale, R.D.: Determining the distribution of probes between different subcellular locations through automated unmixing of subcellular patterns. Proc. Natl. Acad. Sci. U.S.A. 107(7), 2944–2949 (2010)CrossRefGoogle Scholar
  6. 6.
    Hamilton, N.A., Wang, J.T., Kerr, M.C., Teasdale, R.D.: Statistical and visual differentiation of subcellular imaging. BMC Bioinf. 10(1), 94 (2009)CrossRefGoogle Scholar
  7. 7.
    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 17(12), 1213–1223 (2001)CrossRefGoogle Scholar
  8. 8.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)zbMATHCrossRefGoogle Scholar
  9. 9.
    Hu, Y., Murphy, R.F.: Automated interpretation of subcellular patterns from immunofluorescence microscopy. J. Immunol. Methods 290(1), 93–105 (2004)CrossRefGoogle Scholar
  10. 10.
    Hamilton, N.A., Pantelic, R.S., Hanson, K., Teasdale, R.D.: Fast automated cell phenotype image classification. BMC Bioinform. 8(1), 1–8 (2007)CrossRefGoogle Scholar
  11. 11.
    Chang, C.C., Lin, C.J.: Libsvm. (2), 1–27 (2011)CrossRefGoogle Scholar
  12. 12.
    Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19(6), 1657–1663 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  13. 13.
    Nanni, L., Brahnam, S., Lumini, A.: Novel features for automated cell phenotype image classification. Adv. Exp. Med. Biol. 680, 207–213 (2010)zbMATHCrossRefGoogle Scholar
  14. 14.
    Murala, S., Maheshwari, R.P., Balasubramanian, R.: Local tetra patterns: a new feature descriptor for content-based image retrieval. IEEE Trans. Image Process. 21(5), 2874–2886 (2012)MathSciNetzbMATHCrossRefGoogle Scholar
  15. 15.
    Xu, Y.Y., Yang, F., Zhang, Y., Shen, H.B.: Bioimaging-based detection of mislocalized proteins in human cancers by semi-supervised learning. Bioinformatics 31(7), 1111 (2015)CrossRefGoogle Scholar
  16. 16.
    Newberg, J., Murphy, R.: A framework for the automated analysis of subcellular patterns in human protein atlas images. J. Proteome Res. 7(6), 2300–2308 (2008)CrossRefGoogle Scholar
  17. 17.
    Chebira, A., Barbotin, Y., Jackson, C., Merryman, T., Srinivasa, G., Murphy, R.F., Kovačević, J.: A multiresolution approach to automated classification of protein subcellular location images. BMC Bioinf. 8(1), 1–10 (2007)CrossRefGoogle Scholar
  18. 18.
    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 29(16), 2032–2040 (2013)CrossRefGoogle Scholar
  19. 19.
    Zhao, G., Pietikainen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 915–928 (2007)CrossRefGoogle Scholar
  20. 20.
    Ahmed, F., Hossain, E., Bari, A.H., Shihavuddin, A.S.M.: Compound local binary pattern (CLBP) for robust facial expression recognition. In: IEEE International Symposium on Computational Intelligence and Informatics, pp. 391–395 (2011)Google Scholar
  21. 21.
    Oberoi, S., Bakshi, V.: A framework for medical image retrieval using local tetra patterns. Int. J. Eng. Technol. 5(1), 27 (2013)Google Scholar
  22. 22.
    Thangadurai, K.: An improved local tetra pattern for content based image retrieval. J. Global Res. Comput. Sci. 4(4), 37–42 (2013)Google Scholar
  23. 23.
    Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recogn. 37(9), 1757–1771 (2004)CrossRefGoogle Scholar
  24. 24.
    Yang, F., Xu, Y.Y., Shen, H.B.: Many local pattern texture features: which is better for image-based multilabel human protein subcellular localization classification? Sci. World J. 2014(12), 429049 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Communications and ElectronicsJiangxi Science and Technology Normal UniversityNanchangChina

Personalised recommendations