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Bioimage-based protein subcellular location prediction: a comprehensive review

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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.

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References

  1. Domon B, Aebersold R. Options and considerations when selecting a quantitative proteomics strategy. Nature Biotechnology, 2010, 28(7): 710–721

    Article  Google Scholar 

  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): 35–48

    Article  Google Scholar 

  3. Tyers M, Mann M. From genomics to proteomics. Nature, 2003, 422(6928): 193–197

    Article  Google Scholar 

  4. Casci T. Bioinformatics: Next-generation omics. Nature Reviews Genetics, 2012, 13(6): 378–379

    Article  Google Scholar 

  5. Kanehisa M, Bork P. Bioinformatics in the post-sequence era. Nature Genetics, 2003, 33: 305–310

    Article  Google Scholar 

  6. Levine A G. An explosion of bioinformatics careers. Science, 2014, 344(6189): 1303–1306

    Article  Google Scholar 

  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): 697–710

    Article  Google Scholar 

  8. Murphy R F. A new era in bioimage informatics. Bioinformatics, 2014, 30(10): 1353–1353

    Article  Google Scholar 

  9. Peng H. Bioimage informatics: a new area of engineering biology. Bioinformatics, 2008, 24(17): 1827–1836

    Article  Google Scholar 

  10. Chou K-C. Some remarks on predicting multi-label attributes in molecular biosystems. Molecular Biosystems, 2013, 9(6): 1092–1100

    Article  Google Scholar 

  11. Hung M-C, Link W. Protein localization in disease and therapy. Journal of Cell Science, 2011, 124(20): 3381–3392

    Article  Google Scholar 

  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): 19223–19233

    Article  Google Scholar 

  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): 1283–1294

    Article  Google Scholar 

  14. Liu Z, Hu J. Mislocalization-related disease gene discovery using gene expression based computational protein localization prediction. Methods, 2016, 93: 119–127

    Article  Google Scholar 

  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): 262–269

    Article  Google Scholar 

  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): 225–237

    Article  Google Scholar 

  17. Briesemeister S, Rahnenführer J, Kohlbacher O. Going from where to why—interpretable prediction of protein subcellular localization. Bioinformatics, 2010, 26(9): 1232–1238

    Article  Google Scholar 

  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): 153–162

    Article  Google Scholar 

  19. Imai K, Nakai K. Prediction of subcellular locations of proteins: where to proceed? Proteomics, 2010, 10(22): 3970–3983

    Article  Google Scholar 

  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): 561–567

    Article  Google Scholar 

  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): e9931

    Article  Google Scholar 

  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): 1

    Article  Google Scholar 

  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): 1–18

    Article  Google Scholar 

  24. Megason S G, Fraser S E. Imaging in systems biology. Cell, 2007, 130(5): 784–795

    Article  Google Scholar 

  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–S4

    Article  Google Scholar 

  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): 18249–18254

    Article  Google Scholar 

  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): 1111–1119

    Article  Google Scholar 

  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): 2944–2949

    Article  Google Scholar 

  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): 2032–2040

    Article  Google Scholar 

  30. Murphy R F. CellOrganizer: image-derived models of subcellular organization and protein distribution. Methods in Cell Biology, 2012, 110: 179

    Article  Google Scholar 

  31. Murphy R F. Building cell models and simulations from microscope images. Methods, 2015

    Google Scholar 

  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): 315–323

    Article  Google Scholar 

  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: 17–24

    Article  Google Scholar 

  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): 1130–1136

    Article  Google Scholar 

  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): 287–292

    Article  Google Scholar 

  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): 4445–4450

    Article  Google Scholar 

  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): S3

    Article  Google Scholar 

  38. Tahir M, Khan A, Majid A. Protein subcellular localization of fluorescence imagery using spatial and transform domain features. Bioinformatics, 2012, 28(1): 91–97

    Article  Google Scholar 

  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): 2184–2192

    Article  Google Scholar 

  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): 217–224

    Article  Google Scholar 

  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, 4

  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): 1248–1250

    Article  Google Scholar 

  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): 1323–1328

    Article  Google Scholar 

  44. Stephens D J, Allan V J. Light microscopy techniques for live cell imaging. Science, 2003, 300(5616): 82–86

    Article  Google Scholar 

  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): 633–634

    Google Scholar 

  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–D233

    Google Scholar 

  47. Ljosa V, Sokolnicki K L, Carpenter A E. Annotated high-throughput microscopy image sets for validation. Nat Methods, 2012, 9(7): 637

    Article  Google Scholar 

  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): 943–947

    Article  Google Scholar 

  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): 737–741

    Article  Google Scholar 

  50. Pontèn F, Jirström K, Uhlen M. The Human Protein Atlas—a tool for pathology. The Journal of Pathology, 2008, 216(4): 387–393

    Article  Google Scholar 

  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): 379–395

    Article  Google Scholar 

  52. Glory E, Murphy R F. Automated subcellular location determination and high-throughput microscopy. Developmental Cell, 2007, 12(1): 7–16

    Article  Google Scholar 

  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): 366–375

    Article  Google Scholar 

  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): 1081–1087

    Article  Google Scholar 

  55. Hamilton N A, Pantelic R S, Hanson K, Teasdale R D. Fast automated cell phenotype image classification. BMC Bioinformatics, 2007, 8(1): 110

    Article  Google Scholar 

  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): 613–625

    Article  Google Scholar 

  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): 686–691

    Article  Google Scholar 

  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–D508

    Article  Google Scholar 

  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, 8–18

    Chapter  Google Scholar 

  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): e50514

    Article  Google Scholar 

  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, 2015

    Chapter  Google Scholar 

  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): 1213–1223

    Article  Google Scholar 

  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): 2300–2308

    Article  Google Scholar 

  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): 726–734

    Article  Google Scholar 

  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: 65–80

    Article  Google Scholar 

  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, 886–893

    Google Scholar 

  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): 4231–4243

    Article  Google Scholar 

  68. Nanni L, Lumini A, Brahnam S. Survey on LBP based texture descriptors for image classification. Expert Systems with Applications, 2012, 39(3): 3634–3641

    Article  Google Scholar 

  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): 208–219

    Article  Google Scholar 

  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, 2014

    Google Scholar 

  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): 1223–1232

    Article  Google Scholar 

  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: 113–123

    Article  Google Scholar 

  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): 533–544

    Article  MathSciNet  MATH  Google Scholar 

  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): 1657–1663

    Article  MathSciNet  MATH  Google Scholar 

  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): 3374–3381

    Article  Google Scholar 

  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): 1351–1359

    Article  Google Scholar 

  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. 2013

    Google Scholar 

  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): 2343–2349

    Article  Google Scholar 

  79. Lowe D G. Object recognition from local scale-invariant features. In: Proceedings of the 7th IEEE International Conference on Computer Vision. 1999, 1150–1157

    Chapter  Google Scholar 

  80. Nanni L, Lumini A. A reliable method for cell phenotype image classification. Artificial Intelligence in Medicine, 2008, 43(2): 87–97

    Article  Google Scholar 

  81. Jennrich R I, Sampson P. Stepwise discriminant analysis. Statistical Methods for Digital Computers, 1977, 3: 77–95

    Google Scholar 

  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: 307–318

    Google Scholar 

  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): 445–453

    Google Scholar 

  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, 3926–3932

    Google Scholar 

  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): 79

    Google Scholar 

  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, 1023–1026

    Google Scholar 

  87. Pärnamaa T, Parts L. Accurate classification of protein subcellular localization from high throughput microscopy images using deep learning. bioRxiv, 2016: 050757

    Google Scholar 

  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–i39

    Article  Google Scholar 

  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): 1556–1562

    Article  Google Scholar 

  90. Huang K, Murphy R F. Boosting accuracy of automated classification of fluorescence microscope images for location proteomics. BMC Bioinformatics, 2004, 5(1): 78

    Article  Google Scholar 

  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): 210

    Article  Google Scholar 

  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): e1003504

    Article  Google Scholar 

  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): 1006–1011

    Article  Google Scholar 

  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): 269–274

    Article  Google Scholar 

  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): 384–390

    Article  Google Scholar 

  96. Boutell M R, Luo J, Shen X, Brown C M. Learning multi-label scene classification. Pattern Recognition, 2004, 37(9): 1757–1771

    Article  Google Scholar 

  97. Read J, Pfahringer B, Holmes G, Frank E. Classifier chains for multilabel classification. Machine Learning, 2011, 85(3): 333–359

    Article  MathSciNet  Google Scholar 

  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): 539–545

    Article  Google Scholar 

  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): 114–121

    Google Scholar 

  100. Chen X, Murphy R F. Objective clustering of proteins based on subcellular location patterns. BioMed Research International, 2005, 2005(2): 87–95

    Google Scholar 

  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, 298–306

    Google Scholar 

  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–i12

    Article  Google Scholar 

  103. Hamilton N A, Teasdale R D. Visualizing and clustering high throughput sub-cellular localization imaging. BMC Bioinformatics, 2008, 9(1): 81

    Article  Google Scholar 

  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): e1003085

    Article  Google Scholar 

  105. Zhu X, Goldberg A B. Introduction to semi-supervised learning. Synthesis Lectures on Artificial Intelligence andMachine Learning, 2009, 3(1): 1–130

    Article  MATH  Google Scholar 

  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, 414–417

    Google Scholar 

  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, 289–300

    Chapter  Google Scholar 

  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): e1000974

    Article  Google Scholar 

  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): 187–199

    Article  Google Scholar 

  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, 1016–1019

    Google Scholar 

  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): 707–719

    Article  Google Scholar 

  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: e10047

    Article  Google Scholar 

  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, 261–284

    Chapter  Google Scholar 

  114. Pierleoni A, Martelli P L, Fariselli P, Casadio R. BaCelLo: a balanced subcellular localization predictor. Bioinformatics, 2006, 22(14): e408–e416

    Article  Google Scholar 

  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, 27

    Google Scholar 

  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): 25–29

    Article  Google Scholar 

  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): 457–466

    Google Scholar 

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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).

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Authors and Affiliations

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

    Ying-Ying Xu, Li-Xiu Yao & Hong-Bin Shen

  2. Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China

    Ying-Ying Xu, Li-Xiu Yao & Hong-Bin Shen

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  1. Ying-Ying Xu
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Correspondence to Hong-Bin Shen.

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Ying-Ying Xu received the bachelor degree from the Department of Automation Engineering of Northeast University at Qinhuangdao, China in 2011. Currently she is pursuing the PhD degree in Research Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, China. Her research interests include image processing, pattern recognition, and bioinformatics.

Li-Xiu Yao received her PhD degree from Shanghai Institute of Microsystem and Information Technology, Chinese Academic of Sciences, China in 2000. She is an associate professor of Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, China. Her major is bioinformatics and industrial optimization. She has published more than 20 journal papers in these areas.

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 in 2012. Currently, he is a professor of Institute of Image Processing and Pattern Recognition, SJTU. He has published more than 100 journal papers and constructed 35 bioinformatics severs in these areas, and he serves the editorial members of several international journals. He is the awardee of the NSFC Excellent Young Scholars Program in 2012.

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Xu, YY., Yao, LX. & Shen, HB. Bioimage-based protein subcellular location prediction: a comprehensive review. Front. Comput. Sci. 12, 26–39 (2018). https://doi.org/10.1007/s11704-016-6309-5

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  • DOI: https://doi.org/10.1007/s11704-016-6309-5

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