Please wait a minute...

Frontiers of Computer Science

Front. Comput. Sci.    2018, Vol. 12 Issue (1) : 26-39
Bioimage-based protein subcellular location prediction: a comprehensive review
Ying-Ying XU1,2, Li-Xiu YAO1,2, Hong-Bin SHEN1,2()
1. Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China
2. Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
Download: PDF(643 KB)  
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

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     
Corresponding Authors: Hong-Bin SHEN   
Just Accepted Date: 16 November 2016   Online First Date: 07 June 2017    Issue Date: 12 January 2018
 Cite this article:   
Ying-Ying XU,Li-Xiu YAO,Hong-Bin SHEN. Bioimage-based protein subcellular location prediction: a comprehensive review[J]. Front. Comput. Sci., 2018, 12(1): 26-39.
E-mail this article
E-mail Alert
Articles by authors
Ying-Ying XU
Li-Xiu YAO
Hong-Bin SHEN
1 Domon B, Aebersold R. Options and considerations when selecting a quantitative proteomics strategy. Nature Biotechnology, 2010, 28(7): 710–721
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
3 Tyers M, Mann M. From genomics to proteomics. Nature, 2003, 422(6928): 193–197
4 Casci T. Bioinformatics: Next-generation omics. Nature Reviews Genetics, 2012, 13(6): 378–379
5 Kanehisa M, Bork P. Bioinformatics in the post-sequence era. Nature Genetics, 2003, 33: 305–310
6 Levine A G. An explosion of bioinformatics careers. Science, 2014, 344(6189): 1303–1306
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
8 Murphy R F. A new era in bioimage informatics. Bioinformatics, 2014, 30(10): 1353–1353
9 Peng H. Bioimage informatics: a new area of engineering biology. Bioinformatics, 2008, 24(17): 1827–1836
10 Chou K-C. Some remarks on predicting multi-label attributes in molecular biosystems. Molecular Biosystems, 2013, 9(6): 1092–1100
11 Hung M-C, Link W. Protein localization in disease and therapy. Journal of Cell Science, 2011, 124(20): 3381–3392
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
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
14 Liu Z, Hu J. Mislocalization-related disease gene discovery using gene expression based computational protein localization prediction. Methods, 2016, 93: 119–127
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
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
17 Briesemeister S, Rahnenführer J, Kohlbacher O. Going from where to why—interpretable prediction of protein subcellular localization. Bioinformatics, 2010, 26(9): 1232–1238
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
19 Imai K, Nakai K. Prediction of subcellular locations of proteins: where to proceed? Proteomics, 2010, 10(22): 3970–3983
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
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
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
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
24 Megason S G, Fraser S E. Imaging in systems biology. Cell, 2007, 130(5): 784–795
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
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
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
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
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
30 Murphy R F. CellOrganizer: image-derived models of subcellular organization and protein distribution. Methods in Cell Biology, 2012, 110: 179
31 Murphy R F. Building cell models and simulations from microscope images. Methods, 2015
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
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
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
35 Simpson J C, Wellenreuther R, Poustka A, Pepperkok R, Wiemann S. Systematic subcellular localization of novel proteins identified by large- scale cDNA sequencing. EMBO Reports, 2000, 1(3): 287–292
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
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
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
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
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
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
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
44 Stephens D J, Allan V J. Light microscopy techniques for live cell imaging. Science, 2003, 300(5616): 82–86
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
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
47 Ljosa V, Sokolnicki K L, Carpenter A E. Annotated high-throughput microscopy image sets for validation. Nat Methods, 2012, 9(7): 637
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
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
50 Pontèn F, Jirström K, Uhlen M. The Human Protein Atlas—a tool for pathology. The Journal of Pathology, 2008, 216(4): 387–393
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
52 Glory E, Murphy R F. Automated subcellular location determination and high-throughput microscopy. Developmental Cell, 2007, 12(1): 7–16
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<366::AID-CYTO12>3.0.CO;2-R
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
55 Hamilton N A, Pantelic R S, Hanson K, Teasdale R D. Fast automated cell phenotype image classification. BMC Bioinformatics, 2007, 8(1): 110
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
57 Huh W-K, Falvo J V, Gerke L C, Carroll A S, Howson R W, Weissman J S, O’Shea E K. Global analysis of protein localization in budding yeast. Nature, 2003, 425(6959): 686–691
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
76 Zhao T, Velliste M, Boland M V, Murphy R F. Object type recognition for automated analysis of protein subcellular location. IEEE Transactions on Image Processing, 2005, 14(9): 1351–1359
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
78 Coelho L P, Kangas J D, Naik A W, 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): 2343–2349
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
80 Nanni L, Lumini A. A reliable method for cell phenotype image classification. Artificial Intelligence in Medicine, 2008, 43(2): 87–97
81 Jennrich R I, Sampson P. Stepwise discriminant analysis. Statistical Methods for Digital Computers, 1977, 3: 77–95
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
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
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
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
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
87 Pärnamaa T, Parts L. Accurate classification of protein subcellular localization from high throughput microscopy images using deep learning. bioRxiv, 2016: 050757
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
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
90 Huang K, Murphy R F. Boosting accuracy of automated classification of fluorescence microscope images for location proteomics. BMC Bioinformatics, 2004, 5(1): 78
91 Chebira A, Barbotin Y, Jackson C, Merryman T, Srinivasa G, Murphy R F, Kovaˇcvíc J. A multiresolution approach to automated classification of protein subcellular location images. BMC Bioinformatics, 2007, 8(1): 210
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
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
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
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
96 Boutell M R, Luo J, Shen X, Brown C M. Learning multi-label scene classification. Pattern Recognition, 2004, 37(9): 1757–1771
97 Read J, Pfahringer B, Holmes G, Frank E. Classifier chains for multilabel classification. Machine Learning, 2011, 85(3): 333–359
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
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
100 Chen X, Murphy R F. Objective clustering of proteins based on subcellular location patterns. BioMed Research International, 2005, 2005(2): 87–95
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
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
103 Hamilton N A, Teasdale R D. Visualizing and clustering high throughputsub-cellular localization imaging. BMC Bioinformatics, 2008, 9(1): 81
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
105 Zhu X, Goldberg A B. Introduction to semi-supervised learning. Synthesis Lectures on Artificial Intelligence andMachine Learning, 2009, 3(1): 1–130
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
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
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
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
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
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
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
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
114 Pierleoni A, Martelli P L, Fariselli P, Casadio R. BaCelLo: a balanced subcellular localization predictor. Bioinformatics, 2006, 22(14): e408–e416
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
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
[1] Supplementary Material Download
Full text