Table of Contents
International Journal of Proteomics
Volume 2014 (2014), Article ID 147648, 12 pages
http://dx.doi.org/10.1155/2014/147648
Review Article

Protein-Protein Interaction Detection: Methods and Analysis

1Department of CSE, VR Siddhartha Engineering College, Vijayawada 520007, India
2Department of CSE, Mahatma Gandhi Institute of Technology, Hyderabad 500075, India

Received 26 October 2013; Revised 5 December 2013; Accepted 20 December 2013; Published 17 February 2014

Academic Editor: Yaoqi Zhou

Copyright © 2014 V. Srinivasa Rao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. P. Braun and A. C. Gingras, “History of protein-protein interactions: from egg-white to complex networks,” Proteomics, vol. 12, no. 10, pp. 1478–1498, 2012. View at Google Scholar
  2. Y. Ofran and B. Rost, “Analysing six types of protein-protein interfaces,” Journal of Molecular Biology, vol. 325, no. 2, pp. 377–387, 2003. View at Publisher · View at Google Scholar · View at Scopus
  3. I. M. A. Nooren and J. M. Thornton, “Diversity of protein-protein interactions,” The EMBO Journal, vol. 22, no. 14, pp. 3486–3492, 2003. View at Publisher · View at Google Scholar · View at Scopus
  4. A. Zhang, Protein Interaction Networks-Computational Analysis, Cambridge University Press, New York, NY, USA, 2009.
  5. M. Yanagida, “Functional proteomics; current achievements,” Journal of Chromatography B, vol. 771, no. 1-2, pp. 89–106, 2002. View at Publisher · View at Google Scholar · View at Scopus
  6. T. Berggård, S. Linse, and P. James, “Methods for the detection and analysis of protein-protein interactions,” Proteomics, vol. 7, no. 16, pp. 2833–2842, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. C. von Mering, R. Krause, B. Snel et al., “Comparative assessment of large-scale data sets of protein-protein interactions,” Nature, vol. 417, no. 6887, pp. 399–403, 2002. View at Google Scholar · View at Scopus
  8. E. M. Phizicky and S. Fields, “Protein-protein interactions: methods for detection and analysis,” Microbiological Reviews, vol. 59, no. 1, pp. 94–123, 1995. View at Google Scholar · View at Scopus
  9. C. S. Pedamallu and J. Posfai, “Open source tool for prediction of genome wide protein-protein interaction network based on ortholog information,” Source Code for Biology and Medicine, vol. 5, article 8, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. A. K. Dunker, M. S. Cortese, P. Romero, L. M. Iakoucheva, and V. N. Uversky, “Flexible nets: the roles of intrinsic disorder in protein interaction networks,” FEBS Journal, vol. 272, no. 20, pp. 5129–5148, 2005. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Sarmady, W. Dampier, and A. Tozeren, “HIV protein sequence hotspots for crosstalk with host hub proteins,” PLoS ONE, vol. 6, no. 8, Article ID e23293, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. G. Rigaut, A. Shevchenko, B. Rutz, M. Wilm, M. Mann, and B. Seraphin, “A generic protein purification method for protein complex characterization and proteome exploration,” Nature Biotechnology, vol. 17, no. 10, pp. 1030–1032, 1999. View at Publisher · View at Google Scholar · View at Scopus
  13. A.-C. Gavin, M. Bösche, R. Krause et al., “Functional organization of the yeast proteome by systematic analysis of protein complexes,” Nature, vol. 415, no. 6868, pp. 141–147, 2002. View at Publisher · View at Google Scholar · View at Scopus
  14. S. Pitre, M. Alamgir, J. R. Green, M. Dumontier, F. Dehne, and A. Golshani, “Computational methods for predicting protein-protein interactions,” Advances in Biochemical Engineering/Biotechnology, vol. 110, pp. 247–267, 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. J. S. Rohila, M. Chen, R. Cerny, and M. E. Fromm, “Improved tandem affinity purification tag and methods for isolation of protein heterocomplexes from plants,” Plant Journal, vol. 38, no. 1, pp. S172–S181, 2004. View at Publisher · View at Google Scholar · View at Scopus
  16. G. MacBeath and S. L. Schreiber, “Printing proteins as microarrays for high-throughput function determination,” Science, vol. 289, no. 5485, pp. 1760–1763, 2000. View at Google Scholar · View at Scopus
  17. S. W. Michnick, P. H. Ear, C. Landry, M. K. Malleshaiah, and V. Messier, “Protein-fragment complementation assays for large-scale analysis, functional dissection and dynamic studies of protein-protein interactions in living cells,” Methods in Molecular Biology, vol. 756, pp. 395–425, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. J. J. Moresco, P. C. Carvalho, and J. R. Yates III, “Identifying components of protein complexes in C. elegans using co-immunoprecipitation and mass spectrometry,” Journal of Proteomics, vol. 73, no. 11, pp. 2198–2204, 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. G. P. Smith, “Filamentous fusion phage: novel expression vectors that display cloned antigens on the virion surface,” Science, vol. 228, no. 4705, pp. 1315–1317, 1985. View at Google Scholar · View at Scopus
  20. A. H. Y. Tong, B. Drees, G. Nardelli et al., “A combined experimental and computational strategy to define protein interaction networks for peptide recognition modules,” Science, vol. 295, no. 5553, pp. 321–324, 2002. View at Publisher · View at Google Scholar · View at Scopus
  21. A. H. Y. Tong, M. Evangelista, A. B. Parsons et al., “Systematic genetic analysis with ordered arrays of yeast deletion mutants,” Science, vol. 294, no. 5550, pp. 2364–2368, 2001. View at Publisher · View at Google Scholar · View at Scopus
  22. M. R. O'Connell, R. Gamsjaeger, and J. P. Mackay, “The structural analysis of protein-protein interactions by NMR spectroscopy,” Proteomics, vol. 9, no. 23, pp. 5224–5232, 2009. View at Publisher · View at Google Scholar · View at Scopus
  23. G. Gao, J. G. Williams, and S. L. Campbell, “Protein-protein interaction analysis by nuclear magnetic resonance spectroscopy,” Methods in Molecular Biology, vol. 261, pp. 79–92, 2004. View at Google Scholar · View at Scopus
  24. P. Uetz, L. Glot, G. Cagney et al., “A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae,” Nature, vol. 403, no. 6770, pp. 623–627, 2000. View at Publisher · View at Google Scholar · View at Scopus
  25. T. Ito, T. Chiba, R. Ozawa, M. Yoshida, M. Hattori, and Y. Sakaki, “A comprehensive two-hybrid analysis to explore the yeast protein interactome,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 8, pp. 4569–4574, 2001. View at Publisher · View at Google Scholar · View at Scopus
  26. J. I. Semple, C. M. Sanderson, and R. D. Campbell, “The jury is out on “guilt by association” trials,” Brief Funct Genomic Proteomic, vol. 1, no. 1, pp. 40–52, 2002. View at Google Scholar · View at Scopus
  27. P. James, J. Halladay, and E. A. Craig, “Genomic libraries and a host strain designed for highly efficient two-hybrid selection in yeast,” Genetics, vol. 144, no. 4, pp. 1425–1436, 1996. View at Google Scholar · View at Scopus
  28. D. Llères, S. Swift, and A. I. Lamond, “Detecting protein-protein interactions in vivo with FRET using multiphoton fluorescence lifetime imaging microscopy (FLIM),” Current Protocols in Cytometry, vol. 12, Unit12.10, 2007. View at Google Scholar · View at Scopus
  29. S. L. Rutherford, “From genotype to phenotype: buffering mechanisms and the storage of genetic information,” BioEssays, vol. 22, no. 12, pp. 1095–1105, 2000. View at Google Scholar
  30. J. L. Hartman IV, B. Garvik, and L. Hartwell, “Cell biology: principles for the buffering of genetic variation,” Science, vol. 291, no. 5506, pp. 1001–1004, 2001. View at Publisher · View at Google Scholar · View at Scopus
  31. A. Bender and J. R. Pringle, “Use of a screen for synthetic lethal and multicopy suppressee mutants to identify two new genes involved in morphogenesis in Saccharomyces cerevisiae,” Molecular and Cellular Biology, vol. 11, no. 3, pp. 1295–1305, 1991. View at Google Scholar · View at Scopus
  32. S. L. Ooi, X. Pan, B. D. Peyser et al., “Global synthetic-lethality analysis and yeast functional profiling,” Trends in Genetics, vol. 22, no. 1, pp. 56–63, 2006. View at Publisher · View at Google Scholar · View at Scopus
  33. J. A. Brown, G. Sherlock, C. L. Myers et al., “Global analysis of gene function in yeast by quantitative phenotypic profiling,” Molecular Systems Biology, vol. 2, Article ID 2006.0001, 2006. View at Publisher · View at Google Scholar · View at Scopus
  34. H. Berman, K. Henrick, H. Nakamura, and J. L. Markley, “The worldwide Protein Data Bank (wwPDB): ensuring a single, uniform archive of PDB data,” Nucleic Acids Research, vol. 35, no. 1, pp. D301–D303, 2007. View at Publisher · View at Google Scholar · View at Scopus
  35. L. Lu, H. Lu, and J. Skolnick, “Multiprospector: an algorithm for the prediction of protein-protein interactions by multimeric threading,” Proteins, vol. 49, no. 3, pp. 350–364, 2002. View at Publisher · View at Google Scholar · View at Scopus
  36. I. Xenarios, Ł. Salwínski, X. J. Duan, P. Higney, S.-M. Kim, and D. Eisenberg, “DIP, the database of interacting proteins: a research tool for studying cellular networks of protein interactions,” Nucleic Acids Research, vol. 30, no. 1, pp. 303–305, 2002. View at Google Scholar · View at Scopus
  37. R. Hosur, J. Xu, J. Bienkowska, and B. Berger, “IWRAP: an interface threading approach with application to prediction of cancer-related protein-protein interactions,” Journal of Molecular Biology, vol. 405, no. 5, pp. 1295–1310, 2011. View at Publisher · View at Google Scholar · View at Scopus
  38. R. Hosur, J. Peng, A. Vinayagam, U. Stelzl et al., “A computational framework for boosting confidence in high-throughput protein-protein interaction datasets,” Genome Biology, vol. 13, no. 8, p. R76, 2012. View at Google Scholar
  39. Q. C. Zhang, D. Petrey, L. Deng et al., “Structure-based prediction of protein-protein interactions on a genome-wide scale,” Nature, vol. 490, no. 7421, pp. 556–560, 2012. View at Google Scholar
  40. G. T. Valente, M. L. Acencio, C. Martins, and N. Lemke, “The development of a universal in silico predictor of protein-protein interactions,” PLoS ONE, vol. 8, no. 5, Article ID e65587, 2013. View at Google Scholar
  41. A. Ben-Hur and W. S. Noble, “Kernel methods for predicting protein-protein interactions,” Bioinformatics, vol. 21, no. 1, pp. i38–i46, 2005. View at Publisher · View at Google Scholar · View at Scopus
  42. S.-A. Lee, C.-H. Chan, C.-H. Tsai et al., “Ortholog-based protein-protein interaction prediction and its application to inter-species interactions,” BMC Bioinformatics, vol. 9, supplement 12, article S11, 2008. View at Publisher · View at Google Scholar · View at Scopus
  43. R. L. Tatusov, E. V. Koonin, and D. J. Lipman, “A genomic perspective on protein families,” Science, vol. 278, no. 5338, pp. 631–637, 1997. View at Publisher · View at Google Scholar · View at Scopus
  44. V. Memisevic, A. Wallqvist, and J. Reifman, “Reconstituting protein interaction networks using parameter-dependent domain-domain interactions,” BMC Bioinformatics, vol. 14, pp. 154–168, 2013. View at Google Scholar
  45. J. Wojcik and V. Schächter, “Protein-protein interaction map inference using interacting domain profile pairs,” Bioinformatics, vol. 17, supplement 1, pp. S296–S305, 2001. View at Google Scholar · View at Scopus
  46. M. Yamada, M. S. Kabir, and R. Tsunedomi, “Divergent promoter organization may be a preferred structure for gene control in Escherichia coli,” Journal of Molecular Microbiology and Biotechnology, vol. 6, no. 3-4, pp. 206–210, 2003. View at Publisher · View at Google Scholar · View at Scopus
  47. J. Raes, J. O. Korbel, M. J. Lercher et al., “Prediction of effective genome size in meta genomic samples,” Genome Biology, vol. 7, pp. 911–917, 2004. View at Google Scholar
  48. A. J. Enright, I. Illopoulos, N. C. Kyrpides, and C. A. Ouzounis, “Protein interaction maps for complete genomes based on gene fusion events,” Nature, vol. 402, no. 6757, pp. 86–90, 1999. View at Publisher · View at Google Scholar · View at Scopus
  49. E. M. Marcotte, M. Pellegrini, H.-L. Ng, D. W. Rice, T. O. Yeates, and D. Eisenberg, “Detecting protein function and protein-protein interactions from genome sequences,” Science, vol. 285, no. 5428, pp. 751–753, 1999. View at Publisher · View at Google Scholar · View at Scopus
  50. R. Overbeek, M. Fonstein, M. D'Souza, G. D. Push, and N. Maltsev, “The use of gene clusters to infer functional coupling,” Proceedings of the National Academy of Sciences of the United States of America, vol. 96, no. 6, pp. 2896–2901, 1999. View at Publisher · View at Google Scholar · View at Scopus
  51. C. Freiberg, “Novel computational methods in anti-microbial target identification,” Drug Discovery Today, vol. 6, no. 2, pp. S72–S80, 2001. View at Google Scholar · View at Scopus
  52. F. Pazos and A. Valencia, “In silico two-hybrid system for the selection of physically interacting protein pairs,” Proteins, vol. 47, no. 2, pp. 219–227, 2002. View at Publisher · View at Google Scholar · View at Scopus
  53. T. Sato, Y. Yamanishi, M. Kanehisa, K. Horimoto, and H. Toh, “Improvement of the mirrortree method by extracting evolutionary information,” in Sequence and Genome Analysis: Method and Applications, pp. 129–139, Concept Press, 2011. View at Google Scholar
  54. R. A. Craig and L. Liao, “Phylogenetic tree information aids supervised learning for predicting protein-protein interaction based on distance matrices,” BMC Bioinformatics, vol. 8, article 6, 2007. View at Publisher · View at Google Scholar · View at Scopus
  55. K. Srinivas, A. A. Rao, G. R. Sridhar, and S. Gedela, “Methodology for phylogenetic tree construction,” Journal of Proteomics & Bioinformatics, vol. 1, pp. S005–S011, 2008. View at Google Scholar
  56. T. W. Lin, J. W. Wu, and D. Tien-Hao Chang, “Combining phylogenetic profiling-based and machine learning-based techniques to predict functional related proteins,” PLoS ONE, vol. 8, no. 9, Article ID e75940, 2013. View at Google Scholar
  57. A. Grigoriev, “A relationship between gene expression and protein interactions on the proteome scale: analysis of the bacteriophage T7 and the yeast Saccharomyces cerevisiae,” Nucleic Acids Research, vol. 29, no. 17, pp. 3513–3519, 2001. View at Google Scholar · View at Scopus
  58. C. von Mering, L. J. Jensen, B. Snel et al., “STRING: known and predicted protein-protein associations, integrated and transferred across organisms,” Nucleic Acids Research, vol. 33, pp. D433–D437, 2005. View at Publisher · View at Google Scholar · View at Scopus
  59. C. M. Deane, Ł. Salwiński, I. Xenarios, and D. Eisenberg, “Protein interactions: two methods for assessment of the reliability of high throughput observations,” Molecular & Cellular Proteomics, vol. 1, no. 5, pp. 349–356, 2002. View at Google Scholar · View at Scopus
  60. E. Sprinzak, S. Sattath, and H. Margalit, “How reliable are experimental protein-protein interaction data?” Journal of Molecular Biology, vol. 327, no. 5, pp. 919–923, 2003. View at Publisher · View at Google Scholar · View at Scopus
  61. R. Saito, H. Suzuki, and Y. Hayashizaki, “Construction of reliable protein-protein interaction networks with a new interaction generality measure,” Bioinformatics, vol. 19, no. 6, pp. 756–763, 2003. View at Publisher · View at Google Scholar · View at Scopus
  62. R. Saito, H. Suzuki, and Y. Hayashizaki, “Interaction generality, a measurement to assess the reliability of a protein-protein interaction,” Nucleic Acids Research, vol. 30, no. 5, pp. 1163–1168, 2002. View at Google Scholar · View at Scopus
  63. A. Mora and I. M. Donaldson, “Effects of protein interaction data integration, representation and reliability on the use of network properties for drug target prediction,” BMC Bioinformatics, vol. 13, p. 294, 2012. View at Google Scholar
  64. A. M. Edwards, B. Kus, R. Jansen, D. Greenbaum, J. Greenblatt, and M. Gerstein, “Bridging structural biology and genomics: assessing protein interaction data with known complexes,” Trends in Genetics, vol. 18, no. 10, pp. 529–536, 2002. View at Publisher · View at Google Scholar · View at Scopus
  65. R. Jansen, H. Yu, D. Greenbaum et al., “A bayesian networks approach for predicting protein-protein interactions from genomic data,” Science, vol. 302, no. 5644, pp. 449–453, 2003. View at Publisher · View at Google Scholar · View at Scopus
  66. S. Asthana, O. D. King, F. D. Gibbons, and F. P. Roth, “Predicting protein complex membership using probabilistic network reliability,” Genome Research, vol. 14, no. 6, pp. 1170–1175, 2004. View at Publisher · View at Google Scholar · View at Scopus
  67. S. Suthram, T. Shlomi, E. Ruppin, R. Sharan, and T. Ideker, “A direct comparison of protein interaction confidence assignment schemes,” BMC Bioinformatics, vol. 7, article 360, 2006. View at Publisher · View at Google Scholar · View at Scopus
  68. A. Wagner, “How the global structure of protein interaction networks evolves,” Proceedings of the Royal Society B, vol. 270, no. 1514, pp. 457–466, 2003. View at Publisher · View at Google Scholar · View at Scopus
  69. C. Vittorio Cannistraci, G. Alanis-Lobato, and T. Ravasi, “Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding,” Bioinformatics, vol. 29, pp. i199–i209, 2013. View at Google Scholar
  70. C. Stark, B.-J. Breitkreutz, T. Reguly, L. Boucher, A. Breitkreutz, and M. Tyers, “BioGRID: a general repository for interaction datasets,” Nucleic Acids Research, vol. 34, pp. D535–539, 2006. View at Google Scholar · View at Scopus
  71. A. Patil, K. Nakai, and H. Nakamura, “HitPredict: a database of quality assessed protein-protein interactions in nine species,” Nucleic Acids Research, vol. 39, no. 1, pp. D744–D749, 2011. View at Publisher · View at Google Scholar · View at Scopus
  72. A. Chatr-aryamontri, A. Ceol, L. M. Palazzi et al., “MINT: the Molecular INTeraction database,” Nucleic Acids Research, vol. 35, no. 1, pp. D572–D574, 2007. View at Publisher · View at Google Scholar · View at Scopus
  73. H. Hermjakob, L. Montecchi-Palazzi, C. Lewington et al., “IntAct: an open source molecular interaction database,” Nucleic Acids Research, vol. 32, pp. D452–D455, 2004. View at Google Scholar · View at Scopus
  74. C. Prieto and J. de Las Rivas, “APID: agile protein interaction DataAnalyzer,” Nucleic Acids Research, vol. 34, pp. W298–W302, 2006. View at Publisher · View at Google Scholar · View at Scopus
  75. G. D. Bader, I. Donaldson, C. Wolting, B. F. F. Ouellette, T. Pawson, and C. W. V. Hogue, “BIND—the biomolecular interaction network database,” Nucleic Acids Research, vol. 29, no. 1, pp. 242–245, 2001. View at Google Scholar · View at Scopus
  76. M. J. Cowley, M. Pinese, K. S. Kassahn et al., “PINA v2. 0: mining interactome modules,” Nucleic Acids Research, vol. 40, pp. D862–D865, 2012. View at Google Scholar
  77. K. S. Ahmed and S. M. El-Metwally, “Protein function prediction based on protein-protein interactions: a comparative study,” IFMBE Proceedings, vol. 41, pp. 1245–1249, 2014. View at Google Scholar
  78. B. Schwikowski, P. Uetz, and S. Fields, “A network of protein-protein interactions in yeast,” Nature Biotechnology, vol. 18, no. 12, pp. 1257–1261, 2000. View at Publisher · View at Google Scholar · View at Scopus
  79. H. Hishigaki, K. Nakai, T. Ono, A. Tanigami, and T. Takagi, “Assessment of prediction accuracy of protein function from protein-protein interaction data,” Yeast, vol. 18, no. 6, pp. 523–531, 2001. View at Publisher · View at Google Scholar · View at Scopus
  80. C. Lin, D. Jiang, and A. Zhang, “Prediction of protein function using common-neighbors in protein-protein interaction networks,” in Proceedings of the 6th IEEE Symposium on BioInformatics and BioEngineering (BIBE '06), pp. 251–260, October 2006. View at Publisher · View at Google Scholar · View at Scopus
  81. T. Kim, M. Li, K. Ho Ryu, and J. Shin, “Prediction of protein function from protein-protein interaction network by Weighted Graph Mining,” in Proceedings of the 4th International Conference on Bioinformatics & BioMedical Technology (IPCBEE '12), vol. 29, pp. 150–154, 2012.
  82. R. C. Liddington, “Structural basis of protein-protein interactions,” Methods in Molecular Biology, vol. 261, pp. 3–14, 2004. View at Google Scholar · View at Scopus
  83. J. S. Bader, A. Chaudhuri, J. M. Rothberg, and J. Chant, “Gaining confidence in high-throughput protein interaction networks,” Nature Biotechnology, vol. 22, no. 1, pp. 78–85, 2004. View at Publisher · View at Google Scholar · View at Scopus
  84. K. S. Guimarães, R. Jothi, E. Zotenko, and T. M. Przytycka, “Predicting domain-domain interactions using a parsimony approach,” Genome Biology, vol. 7, no. 11, article R104, 2006. View at Publisher · View at Google Scholar · View at Scopus
  85. B. Lehner and A. G. Fraser, “A first-draft human protein-interaction map,” Genome Biology, vol. 5, no. 9, p. R63, 2004. View at Google Scholar · View at Scopus
  86. D. R. Rhodes, S. A. Tomlins, S. Varambally et al., “Probabilistic model of the human protein-protein interaction network,” Nature Biotechnology, vol. 23, no. 8, pp. 951–959, 2005. View at Google Scholar
  87. R. Milo, S. Itzkovitz, N. Kashtan et al., “Superfamilies of evolved and designed networks,” Science, vol. 303, no. 5663, pp. 1538–1542, 2004. View at Publisher · View at Google Scholar · View at Scopus
  88. T. K. B. Gandhi, J. Zhong, S. Mathivanan et al., “Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets,” Nature Genetics, vol. 38, no. 3, pp. 285–293, 2006. View at Publisher · View at Google Scholar · View at Scopus
  89. S. Srihari and H. waileong, “Survey of computational methods for protein complex prediction from protein interaction networks,” Journal of Bioinformatics and Computational Biology, vol. 11, no. 2, Article ID 1230002, 2013. View at Google Scholar
  90. H. W. Mewes, A. Ruepp, F. Theis et al., “MIPS: curated databases and comprehensive secondary data resources in 2010,” Nucleic Acids Research, vol. 39, no. 1, pp. D220–D224, 2011. View at Publisher · View at Google Scholar · View at Scopus
  91. K. Han, B. Park, H. Kim, J. Hong, and J. Park, “HPID: the human protein interaction,” Bioinformatics, vol. 20, no. 15, pp. 2466–2470, 2004. View at Publisher · View at Google Scholar · View at Scopus
  92. J. M. Fernández, R. Hoffmann, and A. Valencia, “iHOP web services,” Nucleic Acids Research, vol. 35, pp. W21–W26, 2007. View at Publisher · View at Google Scholar · View at Scopus