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Frontiers of Computer Science

, Volume 12, Issue 1, pp 26–39 | Cite as

Bioimage-based protein subcellular location prediction: a comprehensive review

  • Ying-Ying Xu
  • Li-Xiu Yao
  • Hong-Bin Shen
Review Article

Abstract

Subcellular localization of proteins can provide key hints to infer their functions and structures in cells. With the breakthrough of recent molecule imaging techniques, the usage of 2D bioimages has become increasingly popular in automatically analyzing the protein subcellular location patterns. Compared with the widely used protein 1D amino acid sequence data, the images of protein distribution are more intuitive and interpretable, making the images a better choice at many applications for revealing the dynamic characteristics of proteins, such as detecting protein translocation and quantification of proteins. In this paper, we systematically reviewed the recent progresses in the field of automated image-based protein subcellular location prediction, and classified them into four categories including growing of bioimage databases, description of subcellular location distribution patterns, classification methods, and applications of the prediction systems. Besides, we also discussed some potential directions in this field.

Keywords

bioimage informatics protein subcellular location prediction global and local features multi-location protein recognition 

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Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61671288, 91530321, and 61603161), National Outstanding Young Scholar Program and the Science and Technology Commission of Shanghai Municipality (16JC1404300).

Supplementary material

11704_2016_6309_MOESM1_ESM.ppt (312 kb)
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References

  1. 1.
    Domon B, Aebersold R. Options and considerations when selecting a quantitative proteomics strategy. Nature Biotechnology, 2010, 28(7): 710–721CrossRefGoogle Scholar
  2. 2.
    Altelaar A F, Munoz J, Heck A J. Next-generation proteomics: towards an integrative view of proteome dynamics. Nature Reviews Genetics, 2013, 14(1): 35–48CrossRefGoogle Scholar
  3. 3.
    Tyers M, Mann M. From genomics to proteomics. Nature, 2003, 422(6928): 193–197CrossRefGoogle Scholar
  4. 4.
    Casci T. Bioinformatics: Next-generation omics. Nature Reviews Genetics, 2012, 13(6): 378–379CrossRefGoogle Scholar
  5. 5.
    Kanehisa M, Bork P. Bioinformatics in the post-sequence era. Nature Genetics, 2003, 33: 305–310CrossRefGoogle Scholar
  6. 6.
    Levine A G. An explosion of bioinformatics careers. Science, 2014, 344(6189): 1303–1306CrossRefGoogle Scholar
  7. 7.
    Eliceiri K W, Berthold M R, Goldberg I G, Ibáñez L, Manjunath B S, Martone M E, Murphy R F, Peng H, Plant A L, Roysam B. Biological imaging software tools. Nature Methods, 2012, 9(7): 697–710CrossRefGoogle Scholar
  8. 8.
    Murphy R F. A new era in bioimage informatics. Bioinformatics, 2014, 30(10): 1353–1353CrossRefGoogle Scholar
  9. 9.
    Peng H. Bioimage informatics: a new area of engineering biology. Bioinformatics, 2008, 24(17): 1827–1836CrossRefGoogle Scholar
  10. 10.
    Chou K-C. Some remarks on predicting multi-label attributes in molecular biosystems. Molecular Biosystems, 2013, 9(6): 1092–1100CrossRefGoogle Scholar
  11. 11.
    Hung M-C, Link W. Protein localization in disease and therapy. Journal of Cell Science, 2011, 124(20): 3381–3392CrossRefGoogle Scholar
  12. 12.
    Komor A C, Schneider C J, Weidmann A G, Barton J K. Cell-selective biological activity of rhodium metalloinsertors correlates with subcellular localization. Journal of the American Chemical Society, 2012, 134(46): 19223–19233CrossRefGoogle Scholar
  13. 13.
    Lee K, Byun K, Hong W, Chuang H-Y, Pack C-G, Bayarsaikhan E, Paek S H, Kim H, Shin H Y, Ideker T. Proteome-wide discovery of mislocated proteins in cancer. Genome Research, 2013, 23(8): 1283–1294CrossRefGoogle Scholar
  14. 14.
    Liu Z, Hu J. Mislocalization-related disease gene discovery using gene expression based computational protein localization prediction. Methods, 2016, 93: 119–127CrossRefGoogle Scholar
  15. 15.
    Lo P-K, Lee J S, Chen H, Reisman D, Berger F G, Sukumar S. Cytoplasmic mislocalization of overexpressed FOXF1 is associated with the malignancy and metastasis of colorectal adenocarcinomas. Experimental and Molecular Pathology, 2013, 94(1): 262–269CrossRefGoogle Scholar
  16. 16.
    Hu M C-T, Lee D-F, Xia W, Golfman L S, Ou-Yang F, Yang J-Y, Zou Y, Bao S, Hanada N, Saso H. IκB kinase promotes tumorigenesis through inhibition of forkhead FOXO3a. Cell, 2004, 117(2): 225–237CrossRefGoogle Scholar
  17. 17.
    Briesemeister S, Rahnenführer J, Kohlbacher O. Going from where to why—interpretable prediction of protein subcellular localization. Bioinformatics, 2010, 26(9): 1232–1238CrossRefGoogle Scholar
  18. 18.
    Chou K-C, Shen H-B. Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms. Nature Protocols, 2008, 3(2): 153–162CrossRefGoogle Scholar
  19. 19.
    Imai K, Nakai K. Prediction of subcellular locations of proteins: where to proceed? Proteomics, 2010, 10(22): 3970–3983CrossRefGoogle Scholar
  20. 20.
    Shen H B, Chou K C. Nuc-PLoc: a new web-server for predicting protein subnuclear localization by fusing PseAA composition and PsePSSM. Protein Engineering, Design and Selection, 2007, 20(11): 561–567CrossRefGoogle Scholar
  21. 21.
    Chou K-C, Shen H-B. A new method for predicting the subcellular localization of eukaryotic proteins with both single and multiple sites: Euk-mPLoc 2.0. PLoS One, 2010, 5(4): e9931CrossRefGoogle Scholar
  22. 22.
    Su E, Chiu H-S, Lo A, Hwang J-K, Sung T-Y, Hsu W-L. Protein subcellular localization prediction based on compartment-specific features and structure conservation. BMC Bioinformatics, 2007, 8(1): 1CrossRefGoogle Scholar
  23. 23.
    Hawkins J, Bodén M. Detecting and sorting targeting peptides with neural networks and support vector machines. Journal of Bioinformatics and Computational Biology, 2006, 4(1): 1–18CrossRefGoogle Scholar
  24. 24.
    Megason S G, Fraser S E. Imaging in systems biology. Cell, 2007, 130(5): 784–795CrossRefGoogle Scholar
  25. 25.
    O’Donoghue S I, Gavin A-C, Gehlenborg N, Goodsell D S, Hériché J-K, Nielsen C B, North C, Olson A J, Procter J B, Shattuck D W. Visualizing biological data—now and in the future. Nature Methods, 2010, 7: S2–S4CrossRefGoogle Scholar
  26. 26.
    Kumar A, Rao A, Bhavani S, Newberg J Y, Murphy R F. Automated analysis of immunohistochemistry images identifies candidate location biomarkers for cancers. Proceedings of the National Academy of Sciences, 2014, 111(51): 18249–18254CrossRefGoogle Scholar
  27. 27.
    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
  28. 28.
    Peng T, Bonamy G M, Glory-Afshar E, Rines D R, Chanda S K, Murphy R F. Determining the distribution of probes between different subcellular locations through automated unmixing of subcellular patterns. Proceedings of the National Academy of Sciences, 2010, 107(7): 2944–2949CrossRefGoogle Scholar
  29. 29.
    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
  30. 30.
    Murphy R F. CellOrganizer: image-derived models of subcellular organization and protein distribution. Methods in Cell Biology, 2012, 110: 179CrossRefGoogle Scholar
  31. 31.
    Murphy R F. Building cell models and simulations from microscope images. Methods, 2015Google Scholar
  32. 32.
    Stadler C, Rexhepaj E, Singan V R, Murphy R F, Pepperkok R, Uhlén M, Simpson J C, Lundberg E. Immunofluorescence and fluorescentprotein tagging show high correlation for protein localization in mammalian cells. Nature Methods, 2013, 10(4): 315–323CrossRefGoogle Scholar
  33. 33.
    Jagadeesh V, Anderson J, Jones B, Marc R, Fisher S, Manjunath B. Synapse classification and localization in electron micrographs. Pattern Recognition Letters, 2014, 43: 17–24CrossRefGoogle Scholar
  34. 34.
    Conrad C, Erfle H, Warnat P, Daigle N, Lörch T, Ellenberg J, Pepperkok R, Eils R. Automatic identification of subcellular phenotypes on human cell arrays. Genome Research, 2004, 14(6): 1130–1136CrossRefGoogle Scholar
  35. 35.
    Simpson J C, Wellenreuther R, Poustka A, Pepperkok R, Wiemann S. Systematic subcellular localization of novel proteins identified by largescale cDNA sequencing. EMBO Reports, 2000, 1(3): 287–292CrossRefGoogle Scholar
  36. 36.
    Knowles D W, Sudar D, Bator-Kelly C, Bissell M J, Lelièvre S A. Automated local bright feature image analysis of nuclear protein distribution identifies changes in tissue phenotype. Proceedings of the National Academy of Sciences of the United States of America, 2006, 103(12): 4445–4450CrossRefGoogle Scholar
  37. 37.
    Long F, Peng H, Sudar D, Lelièvre S A, Knowles D W. Phenotype clustering of breast epithelial cells in confocal images based on nuclear protein distribution analysis. BMC Cell Biology, 2007, 8(Suppl 1): S3CrossRefGoogle Scholar
  38. 38.
    Tahir M, Khan A, Majid A. Protein subcellular localization of fluorescence imagery using spatial and transform domain features. Bioinformatics, 2012, 28(1): 91–97CrossRefGoogle Scholar
  39. 39.
    Xu Y-Y, Yang F, Shen H-B. Incorporating organelle correlations into semi-supervised learning for protein subcellular localization prediction. Bioinformatics, 2016, 32(14): 2184–2192CrossRefGoogle Scholar
  40. 40.
    Giepmans B N, Adams S R, Ellisman M H, Tsien R Y. The fluorescent toolbox for assessing protein location and function. Science, 2006, 312(5771): 217–224CrossRefGoogle Scholar
  41. 41.
    Gough A, Lezon T, Faeder J R, Chennubhotla C, Murphy R F, Critchley-Thorne R, Taylor D L. High content analysis with cellular and tissue systems biology: a bridge between cancer cell biology and tissue-based diagnostics. The Molecular Basis of Cancer, 2014, 4Google Scholar
  42. 42.
    Uhlen M, Oksvold P, Fagerberg L, Lundberg E, Jonasson K, Forsberg M, Zwahlen M, Kampf C, Wester K, Hober S. Towards a knowledge-based human protein atlas. Nature Biotechnology, 2010, 28(12): 1248–1250CrossRefGoogle Scholar
  43. 43.
    Camp R L, Chung G G, Rimm D L. Automated subcellular localization and quantification of protein expression in tissue microarrays. Nature Medicine, 2002, 8(11): 1323–1328CrossRefGoogle Scholar
  44. 44.
    Stephens D J, Allan V J. Light microscopy techniques for live cell imaging. Science, 2003, 300(5616): 82–86CrossRefGoogle Scholar
  45. 45.
    Cho B H, Cao-Berg I, Bakal J A, Murphy R F. OMERO. Searcher: content-based image search for microscope images. Nature Methods, 2012, 9(7): 633–634Google Scholar
  46. 46.
    Sprenger J, Fink J L, Karunaratne S, Hanson K, Hamilton N A, Teasdale R D. LOCATE: a mammalian protein subcellular localization database. Nucleic Acids Research, 2008, 36(Suppl 1): D230–D233Google Scholar
  47. 47.
    Ljosa V, Sokolnicki K L, Carpenter A E. Annotated high-throughput microscopy image sets for validation. Nat Methods, 2012, 9(7): 637CrossRefGoogle Scholar
  48. 48.
    Shamir L, Orlov N, Eckley D M, Macura T J, Goldberg I G. IICBU 2008: a proposed benchmark suite for biological image analysis. Medical & Biological Engineering & Computing, 2008, 46(9): 943–947CrossRefGoogle Scholar
  49. 49.
    Ghaemmaghami S, Huh W-K, Bower K, Howson R W, Belle A, Dephoure N, O’ Shea E K, Weissman J S. Global analysis of protein expression in yeast. Nature, 2003, 425(6959): 737–741CrossRefGoogle Scholar
  50. 50.
    Pontèn F, Jirström K, Uhlen M. The Human Protein Atlas—a tool for pathology. The Journal of Pathology, 2008, 216(4): 387–393CrossRefGoogle Scholar
  51. 51.
    Martone M E, Zhang S, Gupta A, Qian X, He H, Price D L, Wong M, Santini S, Ellisman M H. The cell-centered database. Neuroinformatics, 2003, 1(4): 379–395CrossRefGoogle Scholar
  52. 52.
    Glory E, Murphy R F. Automated subcellular location determination and high-throughput microscopy. Developmental Cell, 2007, 12(1): 7–16CrossRefGoogle Scholar
  53. 53.
    Boland M V, Markey M K, Murphy R F. Automated recognition of patterns characteristic of subcellular structures in fluorescence microscopy images. Cytometry, 1998, 33(3): 366–375CrossRefGoogle Scholar
  54. 54.
    Osuna E G, Hua J, Bateman N W, Zhao T, Berget P B, Murphy R F. Large-scale automated analysis of location patterns in randomly tagged 3T3 cells. Annals of Biomedical Engineering, 2007, 35(6): 1081–1087CrossRefGoogle Scholar
  55. 55.
    Hamilton N A, Pantelic R S, Hanson K, Teasdale R D. Fast automated cell phenotype image classification. BMC Bioinformatics, 2007, 8(1): 110CrossRefGoogle Scholar
  56. 56.
    Aturaliya R N, Fink J L, Davis M J, Teasdale M S, Hanson K A, Miranda K C, Forrest A R, Grimmond S M, Suzuki H, Kanamori M. Subcellular localization of mammalian type II membrane proteins. Traffic, 2006, 7(5): 613–625CrossRefGoogle Scholar
  57. 57.
    Huh W-K, Falvo J V, Gerke LC, Carroll AS, Howson RW, Weissman J S, O’ Shea E K. Global analysis of protein localization in budding yeast. Nature, 2003, 425(6959): 686–691CrossRefGoogle Scholar
  58. 58.
    Bannasch D, Mehrle A, Glatting K H, Pepperkok R, Poustka A, Wiemann S. LIFEdb: a database for functional genomics experiments integrating information from external sources, and serving as a sample tracking system. Nucleic Acids Research, 2004, 32(Suppl 1): D505–D508CrossRefGoogle Scholar
  59. 59.
    Coelho L P, Glory-Afshar E, Kangas J, Quinn S, Shariff A, Murphy R F. Principles of bioimage informatics: focus on machine learning of cell patterns. In: Blaschke C, Shatkay H, eds. Linking Literature, Information, and Knowledge for Biology. Lecture Notes in Computer Science, Vol 6004. Berlin: Springer, 2010, 8–18CrossRefGoogle Scholar
  60. 60.
    Li J, Newberg J Y, Uhlén M, Lundberg E, Murphy R F. Automated analysis and reannotation of subcellular locations in confocal images from the human protein atlas. PloS One, 2012, 7(11): e50514CrossRefGoogle Scholar
  61. 61.
    Li S, Besson S, Blackburn C, Carroll M, Ferguson R K, Flynn H, Gillen K, Leigh R, Lindner D, Linkert M. Metadata management for high content screening in OMERO. Methods, 2015CrossRefGoogle Scholar
  62. 62.
    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
  63. 63.
    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
  64. 64.
    Shariff A, Kangas J, Coelho L P, Quinn S, Murphy R F. Automated image analysis for high-content screening and analysis. Journal of Biomolecular Screening, 2010, 15(7): 726–734CrossRefGoogle Scholar
  65. 65.
    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, 2016, 345: 65–80CrossRefGoogle Scholar
  66. 66.
    Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2005, 886–893Google Scholar
  67. 67.
    Tahir M, Khan A, Majid A, Lumini A. Subcellular localization using fluorescence imagery: utilizing ensemble classification with diverse feature extraction strategies and data balancing. Applied Soft Computing, 2013, 13(11): 4231–4243CrossRefGoogle Scholar
  68. 68.
    Nanni L, Lumini A, Brahnam S. Survey on LBP based texture descriptors for image classification. Expert Systems with Applications, 2012, 39(3): 3634–3641CrossRefGoogle Scholar
  69. 69.
    Paci M, Nanni L, Lahti A, Aalto-Setala K, Hyttinen J, Severi S. Nonbinary coding for texture descriptors in sub-cellular and stem cell image classification. Current Bioinformatics, 2013, 8(2): 208–219CrossRefGoogle Scholar
  70. 70.
    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? The Scientific World Journal, 2014Google Scholar
  71. 71.
    Koh J L, Chong Y T, Friesen H, Moses A, Boone C, Andrews B J, Moffat J. CYCLoPs: a comprehensive database constructed from automated analysis of protein abundance and subcellular localization patterns in Saccharomyces cerevisiae. G3: Genes, Genomes, Genetics, 2015, 5(6): 1223–1232CrossRefGoogle Scholar
  72. 72.
    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: 113–123CrossRefGoogle Scholar
  73. 73.
    Zhang B, Gao Y, Zhao S, Liu J. Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE Transactions on Image Processing, 2010, 19(2): 533–544MathSciNetMATHCrossRefGoogle Scholar
  74. 74.
    Guo Z, Zhang L, Zhang D. A completed modeling of local binary pattern operator for texture classification. IEEE Transactions on Image Processing, 2010, 19(6): 1657–1663MathSciNetMATHCrossRefGoogle Scholar
  75. 75.
    Lin C-C, Tsai Y-S, Lin Y-S, Chiu T-Y, Hsiung C-C, Lee M-I, Simpson J C, Hsu C-N. Boosting multiclass learning with repeating codes and weak detectors for protein subcellular localization. Bioinformatics, 2007, 23(24): 3374–3381CrossRefGoogle Scholar
  76. 76.
    Zhao T, Velliste M, Boland MV, Murphy R F. Object type recognition for automated analysis of protein subcellular location. IEEE Transactions on Image Processing, 2005, 14(9): 1351–1359CrossRefGoogle Scholar
  77. 77.
    Godil A, Lian Z, Wagan A. Exploring local features and the bag-ofvisual-words approach for bioimage classification. In: Proceedings of the ACM International Conference on Bioinformatics, Computational Biology and Biomedical Informatics. 2013Google Scholar
  78. 78.
    Coelho L P, Kangas J D, Naik A W, Osuna-Highley E, Glory-Afshar E, Fuhrman M, Simha R, Berget P B, Jarvik JW, Murphy R F. Determining the subcellular location of new proteins from microscope images using local features. Bioinformatics, 2013, 29(18): 2343–2349CrossRefGoogle Scholar
  79. 79.
    Lowe D G. Object recognition from local scale-invariant features. In: Proceedings of the 7th IEEE International Conference on Computer Vision. 1999, 1150–1157CrossRefGoogle Scholar
  80. 80.
    Nanni L, Lumini A. A reliable method for cell phenotype image classification. Artificial Intelligence in Medicine, 2008, 43(2): 87–97CrossRefGoogle Scholar
  81. 81.
    Jennrich R I, Sampson P. Stepwise discriminant analysis. Statistical Methods for Digital Computers, 1977, 3: 77–95Google Scholar
  82. 82.
    Huang K, Velliste M, Murphy R F. Feature reduction for improved recognition of subcellular location patterns in fluorescence microscope images. Proceedings of SPIE—The International Society for Optical Engineering, 2003, 4962: 307–318Google Scholar
  83. 83.
    Loo L-H, Wu L F, Altschuler S J. Image-based multivariate profiling of drug responses from single cells. Nature Methods, 2007, 4(5): 445–453Google Scholar
  84. 84.
    Kouzani A Z. Subcellular localisation of proteins in fluorescent microscope images using a random forest. In: Proceedings of IEEE International Joint Conference on Neural Networks. 2008, 3926–3932Google Scholar
  85. 85.
    Zhang B, Zhang Y, Lu W, Han G. Phenotype recognition by curvelet transform and random subspace ensemble. Journal of Applied Mathematics and Bioinformatics, 2011, 1(1): 79Google Scholar
  86. 86.
    Newberg J Y, Li J, Rao A, Pontén F, Uhlén M, Lundberg E, Murphy R F. Automated analysis of human protein atlas immunofluorescence images. In: Proceedings of IEEE International Symposium on Biomedical Imaging: From Nano to Macro. 2009, 1023–1026Google Scholar
  87. 87.
    Pärnamaa T, Parts L. Accurate classification of protein subcellular localization from high throughput microscopy images using deep learning. bioRxiv, 2016: 050757Google Scholar
  88. 88.
    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): i32–i39CrossRefGoogle Scholar
  89. 89.
    Nanni L, Lumini A, Lin Y-S, Hsu C-N, Lin C-C. Fusion of systems for automated cell phenotype image classification. Expert Systems with Applications, 2010, 37(2): 1556–1562CrossRefGoogle Scholar
  90. 90.
    Huang K, Murphy R F. Boosting accuracy of automated classification of fluorescence microscope images for location proteomics. BMC Bioinformatics, 2004, 5(1): 78CrossRefGoogle Scholar
  91. 91.
    Chebira A, Barbotin Y, Jackson C, Merryman T, Srinivasa G, Murphy R F, Kova?cvi´c J. A multiresolution approach to automated classification of protein subcellular location images. BMC Bioinformatics, 2007, 8(1): 210CrossRefGoogle Scholar
  92. 92.
    Loo L-H, Laksameethanasan D, Tung Y-L. Quantitative protein localization signatures reveal an association between spatial and functional divergences of proteins. PLoS Comput Biol, 2014, 10(3): e1003504CrossRefGoogle Scholar
  93. 93.
    Shen H B, Chou K C. Hum-mPLoc: an ensemble classifier for largescale human protein subcellular location prediction by incorporating samples with multiple sites. Biochemical & Biophysical Research Communications, 2007, 355(4): 1006–1011CrossRefGoogle Scholar
  94. 94.
    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
  95. 95.
    Zhu L, Yang J, Shen H-B. Multi label learning for prediction of human protein subcellular localizations. The Protein Journal, 2009, 28(9–10): 384–390CrossRefGoogle Scholar
  96. 96.
    Boutell M R, Luo J, Shen X, Brown C M. Learning multi-label scene classification. Pattern Recognition, 2004, 37(9): 1757–1771CrossRefGoogle Scholar
  97. 97.
    Read J, Pfahringer B, Holmes G, Frank E. Classifier chains for multilabel classification. Machine Learning, 2011, 85(3): 333–359MathSciNetCrossRefGoogle Scholar
  98. 98.
    Hu C-D, Kerppola T K. Simultaneous visualization of multiple protein interactions in living cells using multicolor fluorescence complementation analysis. Nature Biotechnology, 2003, 21(5): 539–545CrossRefGoogle Scholar
  99. 99.
    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
  100. 100.
    Chen X, Murphy R F. Objective clustering of proteins based on subcellular location patterns. BioMed Research International, 2005, 2005(2): 87–95Google Scholar
  101. 101.
    Chen X, Velliste M, Weinstein S, Jarvik J W, Murphy R F. Location proteomics: building subcellular location trees from highresolution 3D fluorescence microscope images of randomly tagged proteins. In: Proceedings of SPIE 4962, Manipulation and Analysis of Biomolecules, Cells, and Tissues. 2003, 298–306Google Scholar
  102. 102.
    Coelho L P, Peng T, Murphy R F. Quantifying the distribution of probes between subcellular locations using unsupervised pattern unmixing. Bioinformatics, 2010, 26(12): i7–i12CrossRefGoogle Scholar
  103. 103.
    Hamilton N A, Teasdale R D. Visualizing and clustering high throughput sub-cellular localization imaging. BMC Bioinformatics, 2008, 9(1): 81CrossRefGoogle Scholar
  104. 104.
    Handfield L-F, Chong Y T, Simmons J, Andrews B J, Moses A M. Unsupervised clustering of subcellular protein expression patterns in high-throughput microscopy images reveals protein complexes and functional relationships between proteins. PLoS Comput Biol, 2013, 9(6): e1003085CrossRefGoogle Scholar
  105. 105.
    Zhu X, Goldberg A B. Introduction to semi-supervised learning. Synthesis Lectures on Artificial Intelligence andMachine Learning, 2009, 3(1): 1–130MATHCrossRefGoogle Scholar
  106. 106.
    Lin Y-S, Huang Y-H, Lin C-C, Hsu C-N. Feature space transformation for semi-supervised learning for protein subcellular localization in fluorescence microscopy images. In: Proceedings of IEEE International Symposium on Biomedical Imaging: From Nano to Macro. 2009, 414–417Google Scholar
  107. 107.
    Zhu S, Matsudaira P, Welsch R, Rajapakse J C. Quantification of cytoskeletal protein localization from high-content images. In: Dijkstra T M H, Tsivtsivadze E, Marchiori E, et al., eds. Pattern Recognition in Bioinformatics. Lecture Notes in Computer Science, Vol 6282. Berlin: Springer, 2010, 289–300CrossRefGoogle Scholar
  108. 108.
    Shamir L, Delaney J D, Orlov N, Eckley D M, Goldberg I G. Pattern recognition software and techniques for biological image analysis. PLoS Comput Biol, 2010, 6(11): e1000974CrossRefGoogle Scholar
  109. 109.
    Foster L J, de Hoog C L, Zhang Y, Zhang Y, Xie X, Mootha V K, Mann M. A mammalian organelle map by protein correlation profiling. Cell, 2006, 125(1): 187–199CrossRefGoogle Scholar
  110. 110.
    Buck T E, Rao A, Coelho L P, Fuhrman M H, Jarvik J W, Berget P B, Murphy R F. Cell cycle dependence of protein subcellular location inferred from static, asynchronous images. In: Proceedings of IEEE Annual International Conference on Engineering in Medicine and Biology Society. 2009, 1016–1019Google Scholar
  111. 111.
    Kumar A, Agarwal S, Heyman J A, Matson S, Heidtman M, Piccirillo S, Umansky L, Drawid A, Jansen R, Liu Y. Subcellular localization of the yeast proteome. Genes & Development, 2002, 16(6): 707–719CrossRefGoogle Scholar
  112. 112.
    Naik A W, Kangas J D, Sullivan D P, Murphy R F. Active machine learning-driven experimentation to determine compound effects on protein patterns. eLife, 2016, 5: e10047CrossRefGoogle Scholar
  113. 113.
    Nair R, Rost B. Predicting protein subcellular localization using intelligent systems. In: Markel S, León D, eds. Silico Technology in Drug Target Identification and Validation. Boca Raton, FL: CRC Press, 2006, 261–284CrossRefGoogle Scholar
  114. 114.
    Pierleoni A, Martelli P L, Fariselli P, Casadio R. BaCelLo: a balanced subcellular localization predictor. Bioinformatics, 2006, 22(14): e408–e416CrossRefGoogle Scholar
  115. 115.
    Winsnes C F, Sullivan D P, Smith K, Lundberg E. Multi-label prediction of subcellular localization in confocal images using deep neural networks. Molecular Biology of the Cell, 2016, 27Google Scholar
  116. 116.
    Ashburner M, Ball C A, Blake J A, Botstein D, Butler H, Cherry J M, Davis A P, Dolinski K, Dwight S S, Eppig J T. Gene ontology: tool for the unification of biology. Nature Genetics, 2000, 25(1): 25–29CrossRefGoogle Scholar
  117. 117.
    Shariff A, Murphy R F, Rohde G K. A generative model of microtubule distributions, and indirect estimation of its parameters from fluorescence microscopy images. Cytometry Part A, 2010, 77(5): 457–466Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2018

Authors and Affiliations

  1. 1.Institute of Image Processing and Pattern RecognitionShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghaiChina

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