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Predicting protein subchloroplast locations: the 10th anniversary

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Abstract

Chloroplast is a type of subcellular organelle in green plants and algae. It is the main subcellular organelle for conducting photosynthetic process. The proteins, which localize within the chloroplast, are responsible for the photosynthetic process at molecular level. The chloroplast can be further divided into several compartments. Proteins in different compartments are related to different steps in the photosynthetic process. Since the molecular function of a protein is highly correlated to the exact cellular localization, pinpointing the subchloroplast location of a chloroplast protein is an important step towards the understanding of its role in the photosynthetic process. Experimental process for determining protein subchloroplast location is always costly and time consuming. Therefore, computational approaches were developed to predict the protein subchloroplast locations from the primary sequences. Over the last decades, more than a dozen studies have tried to predict protein subchloroplast locations with machine learning methods. Various sequence features and various machine learning algorithms have been introduced in this research topic. In this review, we collected the comprehensive information of all existing studies regarding the prediction of protein subchloroplast locations. We compare these studies in the aspects of benchmarking datasets, sequence features, machine learning algorithms, predictive performances, and the implementation availability. We summarized the progress and current status in this special research topic. We also try to figure out the most possible future works in predicting protein subchloroplast locations. We hope this review not only list all existing works, but also serve the readers as a useful resource for quickly grasping the big picture of this research topic. We also hope this review work can be a starting point of future methodology studies regarding the prediction of protein subchloroplast locations.

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Article 25 November 2021

Sujatha Thankeswaran Parvathy, Varatharajalu Udayasuriyan & Vijaipal Bhadana

References

  1. Murphy R F. Automated interpretation of protein subcellular location patterns: implications for early cancer detection and assessment. Annals of the New York Academy of Sciences, 2004, 1020: 124–131

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Zhao Y, Wang J, Guo M, Zhang Z, Yu G. Protein function prediction based on zero-one matrix factorization. SCIENTIA SINICA Informationis, 2019, 49(9): 1159–1174

    Article  Google Scholar 

  4. Wang Z, Zhao C, Wang Y, Sun Z, Wang N. PANDA: protein function prediction using domain architecture and affinity propagation. Scientific Reports, 2018, 8(1): 1–10

    Google Scholar 

  5. Kulmanov M, Hoehndorf R. DeepGOPlus: improved protein function prediction from sequence. Bioinformatics, 2020, 36(2): 422–429

    Article  Google Scholar 

  6. Yu G, Wang K, Domeniconi C, Guo M, Wang J. Isoform function prediction based on bi-random walks on a heterogeneous network. Bioinformatics, 2020, 36(1): 303–310

    Article  Google Scholar 

  7. Reinhardt A, Hubbard T. Using neural networks for prediction of the subcellular location of proteins. Nucleic Acids Research, 1998, 26(1): 2230–2236

    Article  Google Scholar 

  8. Raju T N K. The Nobel chronicles. The Lancet, 2000, 356: 261

    Article  Google Scholar 

  9. Bacia K. Intracellular transport mechanisms: Nobel prize for medicine 2013. Angewandte Chemie International Edition, 2013, 52(48): 12486–12488

    Article  Google Scholar 

  10. Friedrich M J. 2013 Nobel prize recognizes work of scientists who illuminated molecular transport system of cells. JAMA: The Journal of the American Medical Association, 2013, 310(19): 2027–2029

    Article  Google Scholar 

  11. Wickner W T. Profile of Thomas Sudhof, James Rothman, And Randy Schekman, 2013 Nobel laureates in physiology or medicine. Proceedings of the National Academy of Sciences of the United States of America, 2013, 110(46): 18349–18350

    Article  Google Scholar 

  12. Thul P J, Åesson L, Wiking M, Mahdessian D, Geladaki A, AitBlal H, Alm T, Asplund A, Björk L, Breckels LM, Bäckström A, Danielsson F, Fagerberg L, Fall J, Gatto L, Gnann C, Hober S, Hjelmare M, Johansson F, Lee S, Lindskog C, Mulder J, Mulvey CM, Nilsson P, Oksvold P, Rockberg J, Schutten R, Schwenk J M, Sivertsson Å, Sjöstedt E, Skogs M, Stadler C, Sullivan D P, Tegel H, Winsnes C, Zhang C, Zwahlen M, Mardinoglu A, Pontén F, von Feilitzen K, Lilley K S, Uhlén M, Lundberg E. A subcellular map of the human proteome. Science, 2017, 356(6340): eaal3321

    Article  Google Scholar 

  13. Horwitz R, Johnson G T. Whole cell maps chart a course for 21st-century cell biology. Science, 2017, 356(6340): 806–807

    Article  Google Scholar 

  14. Chou K C, Shen H B. Plant-mPLoc: a top-down strategy to augment the power for predicting plant protein subcellular localization. PLoS ONE, 2010, 5(6): e11335

    Article  Google Scholar 

  15. 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 

  16. Shen H B, Chou K C. Virus-mPLoc: a fusion classifier for viral protein subcellular location prediction by incorporating multiple sites. Journal of Biomolecular Structure & Dynamics, 2010, 28(2): 175–186

    Article  Google Scholar 

  17. Shen H B, Chou K C. Gneg-mPLoc: a top-down strategy to enhance the quality of predicting subcellular localization of Gram-negative bacterial proteins. Journal of Theoretical Biology, 2010, 264(2): 326–333

    Article  MATH  Google Scholar 

  18. Chou K C, Wu Z C, Xiao X. ILoc-Euk: a multi-label classifier for predicting the subcellular localization of singleplex and multiplex eukaryotic proteins. PLoS ONE, 2011, 6(3): e18258

    Article  Google Scholar 

  19. Chou K C, Wu Z C, Xiao X. ILoc-Hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites. Molecular BioSystems, 2012, 8(2): 629–641

    Article  Google Scholar 

  20. Wu Z C, Xiao X, Chou K C. ILoc-Plant: a multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites. Molecular BioSystems, 2011, 7(12): 3287–3297

    Article  Google Scholar 

  21. Wu Z C, Xiao X, Chou K C. ILoc-Gpos: a multi-layer classifier for predicting the subcellular localization of singleplex and multiplex Grampositive bacterial proteins. Protein and Peptide Letters, 2012, 19(1): 4–14

    Article  Google Scholar 

  22. Xiao X, Wu Z C, Chou K C. ILoc-Virus: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites. Journal of Theoretical Biology, 2011, 284(1): 42–51

    Article  MATH  Google Scholar 

  23. Lin W Z, Fang J A, Xiao X, Chou K C. ILoc-Animal: a multi-label learning classifier for predicting subcellular localization of animal proteins. Molecular BioSystems, 2013, 9(4): 634–644

    Article  Google Scholar 

  24. 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 

  25. Du P, Wang L. Predicting human protein subcellular locations by the ensemble of multiple predictors via protein-protein interaction network with edge clustering coefficients. PLoS ONE, 2014, 9(1): e86879

    Article  Google Scholar 

  26. Cheng X, Zhao S G, Lin W Z, Xiao X, Chou K C. PLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites. Bioinformatics, 2017, 33(22): 3524–3531

    Article  Google Scholar 

  27. 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, 2017, 33(6): 843–853

    Article  Google Scholar 

  28. Wang Z, Zou Q, Jiang Y, Ju Y, Zeng X. Review of protein subcellular localization prediction. Current Bioinformatics, 2014, 9(3): 331–342

    Article  Google Scholar 

  29. Du P, Li T, Wang X. Recent progress in predicting protein sub-subcellular locations. Expert Review of Proteomics, 2011, 8(3): 391–404

    Article  Google Scholar 

  30. 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 & Selection: PEDS, 2007, 20(11): 561–567

    Article  Google Scholar 

  31. Han G S, Yu Z G, Anh V, Krishnajith A P D, Tian Y C. An ensemble method for predicting subnuclear localizations from primary protein structures. PLoS ONE, 2013, 8(2): e57225

    Article  Google Scholar 

  32. Jiao Y S, Du P F. Predicting protein submitochondrial locations by incorporating the positional-specific physicochemical properties into Chou’s general pseudo-amino acid compositions. Journal of Theoretical Biology, 2017, 416: 81–87

    Article  Google Scholar 

  33. Du P F. Predicting protein submitochondrial locations: the 10th anniversary. Current Genomics, 2017, 18(4): 316–321

    Article  Google Scholar 

  34. Du P, Li Y. Prediction of protein submitochondria locations by hybridizing pseudo-amino acid composition with various physicochemical features of segmented sequence. BMC Bioinformatics, 2006, 7: 518

    Article  Google Scholar 

  35. Ahmad K, Waris M, Hayat M. Prediction of protein submitochondrial locations by incorporating dipeptide composition into Chou’s general pseudo amino acid composition. The Journal of Membrane Biology, 2016, 249(3): 293–304

    Article  Google Scholar 

  36. Zhao W, Li G P, Wang J, Zhou Y K, Gao Y, Du P F. Predicting protein sub-Golgi locations by combining functional domain enrichment scores with pseudo-amino acid compositions. Journal of Theoretical Biology, 2019, 473: 38–43

    Article  MATH  Google Scholar 

  37. Jiao Y S, Du P F. Prediction of Golgi-resident protein types using general form of Chou’s pseudo-amino acid compositions: approaches with minimal redundancy maximal relevance feature selection. Journal of Theoretical Biology, 2016, 402: 38–44

    Article  MATH  Google Scholar 

  38. Jiao Y S, Du P F. Predicting Golgi-resident protein types using pseudo amino acid compositions: approaches with positional specific physicochemical properties. Journal of Theoretical Biology, 2016, 391: 35–42

    Article  MATH  Google Scholar 

  39. Ding H, Guo S H, Deng E Z, Yuan L F, Guo F B, Huang J, Rao N, Chen W, Lin H. Prediction of Golgi-resident protein types by using feature selection technique. Chemometrics and Intelligent Laboratory Systems, 2013, 124: 9–13

    Article  Google Scholar 

  40. Ding H, Liu L, Guo F B, Huang J, Lin H. Identify Golgi protein types with modified Mahalanobis discriminant algorithm and pseudo amino acid composition. Protein and Peptide Letters, 2011, 18(1): 58–63

    Article  Google Scholar 

  41. Rahman M S, Rahman M K, Kaykobad M, Rahman M S. IsGPT: an optimized model to identify sub-Golgi protein types using SVM and Random forest based feature selection. Artificial Intelligence in Medicine, 2018, 84: 90–100

    Article  Google Scholar 

  42. Chou W C, Yin Y, Xu Y. GolgiP: prediction of Golgi-resident proteins in plants. Bioinformatics, 2010, 26(19): 2464–2465

    Article  Google Scholar 

  43. van Dijk A D J, Bosch D, ter Braak C J F, van der Krol A R, van Ham R C H J. Predicting sub-Golgi localization of type II membrane proteins. Bioinformatics, 2008, 24(16): 1779–1786

    Article  Google Scholar 

  44. Du P, Cao S, Li Y. SubChlo: predicting protein subchloroplast locations with pseudo-amino acid composition and the evidence-theoretic K-nearest neighbor (ET-KNN) algorithm. Journal of Theoretical Biology, 2009, 261(2): 330–335

    Article  MATH  Google Scholar 

  45. Denoeux T. A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Transactions on Systems, Man, and Cybernetics, 1995, 25(5): 804–813

    Article  Google Scholar 

  46. Wang X, Zhang W, Zhang Q, Li G Z. MultiP-SChlo: multi-label protein subchloroplast localization prediction with Chou’s pseudo amino acid composition and a novel multi-label classifier. Bioinformatics, 2015, 31(16): 2639–2645

    Article  Google Scholar 

  47. Savojardo C, Martelli P L, Fariselli P, Casadio R. SChloro: directing viridiplantae proteins to six chloroplastic sub-compartments. Bioinformatics, 2017, 33(3): 347–353

    Article  Google Scholar 

  48. Chou K C. Some remarks on protein attribute prediction and pseudo amino acid composition. Journal of Theoretical Biology, 2011, 273(1): 236–247

    Article  MathSciNet  MATH  Google Scholar 

  49. UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Research, 2015, 43(D1): D204–D212

    Article  Google Scholar 

  50. Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics, 2012, 28(23): 3150–3152

    Article  Google Scholar 

  51. Lin H, Chen W, Yuan L F, Li Z Q, Ding H. Using over-represented tetrapeptides to predict protein submitochondria locations. ActaBiotheoretica, 2013, 61(2): 259–268

    Google Scholar 

  52. Tung C W, Liaw C, Ho S J, Ho S Y. Prediction of protein subchloroplast locations using random forests. World Academy of Science, Engineering and Technology, 2010, 65: 903–907

    Google Scholar 

  53. Hu J, Yan X. BS-KNN: an effective algorithm for predicting protein subchloroplast localization. Evolutionary Bioinformatics Online, 2012, 8: 79–87

    Google Scholar 

  54. Saravanan V, Lakshmi P T V. SCLAP: an adaptive boosting method for predicting subchloroplast localization of plant proteins. OMICS: A Journal of Integrative Biology, 2013, 17(2): 106–115

    Article  Google Scholar 

  55. Wang G, Dunbrack Jr R L. PISCES: a protein sequence culling server. Bioinformatics, 2003, 19(12): 1589–1591

    Article  Google Scholar 

  56. Chou K C, Shen H B. Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites. Journal of Proteome Research, 2007, 6(5): 1728–1734

    Article  Google Scholar 

  57. Zhao W, Wang L, Zhang T X, Zhao Z N, Du P F. A brief review on software tools in generating chou’s pseudo-factor representations for all types of biological sequences. Protein and Peptide Letters, 2018, 25(9): 822–829

    Article  Google Scholar 

  58. Lin H, Ding C, Yuan L F, Chen W, Ding H, Li Z Q, Guo F B, Huang J, Rao N N. Predicting subchloroplast locations of proteins based on the general form of chou’s pseudo amino acid composition: approached from optimal tripeptide composition. International Journal of Biomathematics, 2013, 6(2): 1350003

    Article  MathSciNet  Google Scholar 

  59. Du P, Xu C. Predicting multisite protein subcellular locations: progress and challenges. Expert Review of Proteomics, 2013, 10(3): 227–237

    Article  Google Scholar 

  60. Huang C, Yuan J Q. Predicting protein subchloroplast locations with both single and multiple sites via three different modes of Chou’s pseudo amino acid compositions. Journal of Theoretical Biology, 2013, 335: 205–212

    Article  MATH  Google Scholar 

  61. Wan S, Duan Y, Zou Q. HPSLPred: an ensemble multi-label classifier for human protein subcellular location prediction with unbalanced source. Proteomics, 2017, 17(17–18): 1700262

    Article  Google Scholar 

  62. Hussain W, Khan Y D, Rasool N, Khan S A, Chou K C. SPalmitoylC-PseAAC: a sequence-based model developed via Chou’s 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins. Analytical Biochemistry, 2019, 568: 14–23

    Article  MATH  Google Scholar 

  63. Le N Q K, Yapp E K Y, Ho Q T, Nagasundaram N, Ou Y Y, Yeh H Y. IEnhancer-5Step: identifying enhancers using hidden information of DNA sequences via Chou’s 5-step rule and word embedding. Analytical Biochemistry, 2019, 571: 53–61

    Article  Google Scholar 

  64. Chou K C. Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins, 2001, 43(3): 246–255

    Article  Google Scholar 

  65. Chen J, Long R, Wang X L, Liu B, Chou K C. DRHP-PseRA: detecting remote homology proteins using profile-based pseudo protein sequence and rank aggregation. Scientific Reports, 2016, 6: 32333

    Article  Google Scholar 

  66. Chen Q Y, Tang J, Du P F. Predicting protein lysine phosphoglycerylation sites by hybridizing many sequence based features. Molecular Biosystems, 2017, 13(5): 874–882

    Article  Google Scholar 

  67. Huang Y A, You Z H, Chen X, Yan G Y. Improved protein-protein interactions prediction via weighted sparse representation model combining continuous wavelet descriptor and PseAA composition. BMC Systems Biology, 2016, 10(4): 485–494

    Google Scholar 

  68. Jia J, Zhang L, Liu Z, Xiao X, Chou K C. PSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC. Bioinformatics, 2016, 32(20): 3133–3141

    Article  Google Scholar 

  69. Lei G C, Tang J, Du P F. Predicting S-sulfenylation sites using physicochemical properties differences. Letters in Organic Chemistry, 2017, 14(9): 665–672

    Article  Google Scholar 

  70. Du P, Wang X, Xu C, Gao Y. PseAAC-Builder: a cross-platform standalone program for generating various special Chou’s pseudo-amino acid compositions. Analytical Biochemistry, 2012, 425(2): 117–119

    Article  Google Scholar 

  71. Du P, Gu S, Jiao Y. PseAAC-General: fast building various modes of general form of Chou’s pseudo-amino acid composition for large-scale protein datasets. International Journal of Molecular Sciences, 2014, 15(3): 3495–3506

    Article  Google Scholar 

  72. Du P F, Zhao W, Miao Y Y, Wei L Y, Wang L. UltraPse: a universal and extensible software platform for representing biological sequences. International Journal of Molecular Sciences, 2017, 18(11): 2400

    Article  Google Scholar 

  73. Cao D S, Xu Q S, Liang Y Z. Propy: a tool to generate various modes of Chou’s PseAAC. Bioinformatics, 2013, 29(7): 960–962

    Article  Google Scholar 

  74. Liu B, Liu F, Wang X, Chen J, Fang L, Chou K C. Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Research, 2015, 43(W1): W65–W71

    Article  Google Scholar 

  75. Chen Z, Zhao P, Li F, Leier A, Marquez-Lago T T, Wang Y, Webb G I, Smith A I, Daly R J, Chou K C, Song J. IFeature: a Python package and web server for features extraction and selection from protein and peptide sequences. Bioinformatics, 2018, 34(14): 2499–2502

    Article  Google Scholar 

  76. Chou K C. Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology. Current Proteomics, 2009, 6(4): 262–274

    Article  Google Scholar 

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

    Article  Google Scholar 

  78. Chou K C. Impacts of bioinformatics to medicinal chemistry. Medicinal Chemistry, 2015, 11(3): 218–234

    Article  Google Scholar 

  79. Du P, Yu Y. SubMito-PSPCP: predicting protein submitochondrial locations by hybridizing positional specific physicochemical properties with pseudoamino acid compositions. Biomed Research International, 2013, 2013: 263829

    Article  Google Scholar 

  80. Miao Y Y, Zhao W, Li G P, Gao Y, Du P F. Predicting endoplasmic reticulum resident proteins using auto-cross covariance transformation with a U-shaped residue weight-transfer function. Frontiers in Genetics, 2019, 10: 1231

    Article  Google Scholar 

  81. Du P, Li T, Wang X, Xu C. SubChlo-GO: predicting protein subchloroplast locations with weighted gene ontology scores. Current Bioinformatics, 2013, 8(2): 193–199

    Article  Google Scholar 

  82. Carr K, Murray E, Armah E, He R L, Yau S S T. A rapid method for characterization of protein relatedness using feature vectors. PLoS ONE, 2010, 5(3): e9550

    Article  Google Scholar 

  83. Dubchak I, Muchnik I, Mayor C, Dralyuk I, Kim S H. Recognition of a protein fold in the context of the structural classification of proteins (SCOP) classification. Proteins, 1999, 35(4): 401–407

    Article  Google Scholar 

  84. Altschul S F, Madden T L, Schäfer A A, Zhang J, Zhang Z, Miller W, Lipman D J. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Research, 1997, 25(17): 3389–3402

    Article  Google Scholar 

  85. Shi S P, Qiu J D, Sun X Y, Huang J H, Huang S Y, Suo S B, Liang R-P, Zhang L. Identify submitochondria and subchloroplast locations with pseudo amino acid composition: approach from the strategy of discrete wavelet transform feature extraction. Biochimica Et Biophysica Acta, 2011, 1813(3): 424–430

    Article  Google Scholar 

  86. Kawashima S, Pokarowski P, Pokarowska M, Kolinski A, Katayama T, Kanehisa M. AAindex: amino acid index database, progress report 2008. Nucleic Acids Research, 2008, 36 (Database issue): D202–D205

    Article  Google Scholar 

  87. Li X, Wu X, Wu G. Robust feature generation for protein subchloroplast location prediction with a weighted GO transfer model. Journal of Theoretical Biology, 2014, 347: 84–94

    Article  MATH  Google Scholar 

  88. Kyte J, Doolittle R F. A simple method for displaying the hydropathic character of a protein. Journal of Molecular Biology, 1982, 157(1): 105–132

    Article  Google Scholar 

  89. Wan S, Mak M W, Kung S Y. Transductive learning for multi-label protein subchloroplast localization prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2017, 14(1): 212–224

    Article  Google Scholar 

  90. Wan S, Mak M W, Kung S Y. Ensemble linear neighborhood propagation for predicting subchloroplast localization of multi-location proteins. Journal of Proteome Research, 2016, 15(12): 4755–4762

    Article  Google Scholar 

  91. Chou K C, Shen H B. Predicting eukaryotic protein subcellular location by fusing optimized evidence-theoretic K-nearest neighbor classifiers. Journal of Proteome Research, 2006, 5(8): 1888–1897

    Article  Google Scholar 

  92. Chou K C, Shen H B. Hum-PLoc: a novel ensemble classifier for predicting human protein subcellular localization. Biochemical and Biophysical Research Communications, 2006, 347(1): 150–157

    Article  Google Scholar 

  93. Nakai K, Horton P. PSORT: a program for detecting sorting signals in proteins and predicting their subcellular localization. Trends in Biochemical Sciences, 1999, 24(1): 34–36

    Article  Google Scholar 

  94. Zybailov B, Rutschow H, Friso G, Rudella A, Emanuelsson O, Sun Q, van Wijk K J. Sorting signals, N-terminal modifications and abundance of the chloroplast proteome. PLoS ONE, 2008, 3(4): e1994

    Article  Google Scholar 

  95. Andrade M A, O’Donoghue S I, Rost B. Adaptation of protein surfaces to subcellular location. Journal of Molecular Biology, 1998, 276(2): 517–525

    Article  Google Scholar 

  96. Cedano J, Aloy P, Péez-Pons J A, Querol E. Relation between amino acid composition and cellular location of proteins. Journal of Molecular Biology, 1997, 266(3): 594–600

    Article  Google Scholar 

  97. Lv Z, Jin S, Ding H, Zou Q. A random forest sub-golgi protein classifier optimized via dipeptide and amino acid composition features. Frontiers in Bioengineering and Biotechnology, 2019, 7: 215

    Article  Google Scholar 

  98. Jiao Y, Du P. Performance measures in evaluating machine learning based bioinformatics predictors for classifications. Quantitative Biology, 2016, 4(4): 320–330

    Article  Google Scholar 

  99. Cabarle F G C, de la Cruz R T A, Cailipan D P P, Zhang D, Liu X, Zeng X. On solutions and representations of spiking neural P systems with rules on synapses. Information Sciences, 2019, 501: 30–49

    Article  MathSciNet  MATH  Google Scholar 

  100. Xu H, Zeng W, Zhang D, Zeng X. MOEA/HD: a multiobjective evolutionary algorithm based on hierarchical decomposition. IEEE Transactions on Cybernetics, 2019, 49(2): 517–526

    Article  Google Scholar 

  101. Zou Q, Lin G, Jiang X, Liu X, Zeng X. Sequence clustering in bioinformatics: an empirical study. Briefings in Bioinformatics, 2020, 21(1): 1–10

    Google Scholar 

  102. Zeng X, Liu L, Lü L, Zou Q. Prediction of potential disease-associated microRNAs using structural perturbation method. Bioinformatics, 2018, 34(14): 2425–2432

    Article  Google Scholar 

  103. Zeng X, Lin W, Guo M, Zou Q. A comprehensive overview and evaluation of circular RNA detection tools. PLoS Computational Biology, 2017, 13(6): e1005420

    Article  Google Scholar 

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Acknowledgements

This work was supported by National Key R&D Program of China (2018YFC0910405), The National Natural Science Foundation of China (NSFC, Grant No. 61872268); and Open Project Funding of CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences (CASNDST201705).

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  1. College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China

    Jian Sun & Pu-Feng Du

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  1. Jian Sun
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Correspondence to Pu-Feng Du.

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Jian Sun is a master student in the College of Intelligence and Computing, Tianjin University, China. He received his bachelor’s degree in Chemical Engineering from Qingdao University of Science and Technology, China. He expects to receive his master’s degree in 2021.

Pu-Feng Du is an associate professor in the College of Intelligence and Computing, Tianjin University, China. He received his bachelor’s degree and PhD from Tsinghua University, China in 2005 and 2010, respectively. Dr. Du’s research interests include bioinformatics and machine learning.

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Sun, J., Du, PF. Predicting protein subchloroplast locations: the 10th anniversary. Front. Comput. Sci. 15, 152901 (2021). https://doi.org/10.1007/s11704-020-9507-0

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