On Computationally-Enhanced Visual Analysis of Heterogeneous Data and Its Application in Biomedical Informatics

  • Cagatay Turkay
  • Fleur Jeanquartier
  • Andreas Holzinger
  • Helwig Hauser
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8401)

Abstract

With the advance of new data acquisition and generation technologies, the biomedical domain is becoming increasingly data-driven. Thus, understanding the information in large and complex data sets has been in the focus of several research fields such as statistics, data mining, machine learning, and visualization. While the first three fields predominantly rely on computational power, visualization relies mainly on human perceptual and cognitive capabilities for extracting information. Data visualization, similar to Human–Computer Interaction, attempts an appropriate interaction between human and data to interactively exploit data sets. Specifically within the analysis of complex data sets, visualization researchers have integrated computational methods to enhance the interactive processes. In this state-of-the-art report, we investigate how such an integration is carried out. We study the related literature with respect to the underlying analytical tasks and methods of integration. In addition, we focus on how such methods are applied to the biomedical domain and present a concise overview within our taxonomy. Finally, we discuss some open problems and future challenges.

Keywords

Visualization Visual Analytics Heterogenous Data Complex Data Future Challenges Open Problems 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Card, S.K., Mackinlay, J.D., Shneiderman, B.: Information Visualization: Using Vision to Think. Morgan Kaufmann, San Francisco (1999)Google Scholar
  2. 2.
    Moeller, T., Hamann, B., Russell, R.D.: Mathematical foundations of scientific visualization, computer graphics, and massive data exploration. Springer (2009)Google Scholar
  3. 3.
    Ward, M., Grinstein, G., Keim, D.: Interactive data visualization: Foundations, techniques, and applications. AK Peters, Ltd. (2010)Google Scholar
  4. 4.
    Holzinger, A., Dehmer, M., Jurisica, I.: Knowledge discovery and interactive data mining in bioinformatics - state-of-the-art, future challenges and research directions. BMC Bioinformatics 15(suppl. 6), I1 (2014)Google Scholar
  5. 5.
    Johnson, R., Wichern, D.: Applied multivariate statistical analysis, vol. 6. Prentice Hall, Upper Saddle River (2007)MATHGoogle Scholar
  6. 6.
    Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison-Wesley Longman Publishing Co., Inc. (2005)Google Scholar
  7. 7.
    Alpaydin, E.: Introduction to machine learning. MIT press (2004)Google Scholar
  8. 8.
    Keim, D.: Information visualization and visual data mining. IEEE Transactions on Visualization and Computer Graphics 8(1), 1–8 (2002)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Shneiderman, B.: Inventing discovery tools: Combining information visualization with data mining. Information Visualization 1(1), 5–12 (2002)CrossRefMATHGoogle Scholar
  10. 10.
    Ma, K.L.: Machine learning to boost the next generation of visualization technology. IEEE Computer Graphics and Applications 27(5), 6–9 (2007)CrossRefGoogle Scholar
  11. 11.
    Tukey, J.W.: Exploratory Data Analysis. Addison-Wesley (1977)Google Scholar
  12. 12.
    Cleveland, W.S., Mac Gill, M.E.: Dynamic graphics for statistics. CRC Press (1988)Google Scholar
  13. 13.
    Thomas, J.J., Cook, K.A.: Illuminating the Path: The Research and Development Agenda for Visual Analytics. National Visualization and Analytics Ctr (2005)Google Scholar
  14. 14.
    Keim, D.A., Kohlhammer, J., Ellis, G., Mansmann, F.: Mastering The Information Age-Solving Problems with Visual Analytics. Florian Mansmann (2010)Google Scholar
  15. 15.
    Bertini, E., Lalanne, D.: Investigating and reflecting on the integration of automatic data analysis and visualization in knowledge discovery. SIGKDD Explor. Newsl. 11(2), 9–18 (2010)CrossRefGoogle Scholar
  16. 16.
    Keim, D.A., Andrienko, G., Fekete, J.-D., Görg, C., Kohlhammer, J., Melançon, G.: Visual analytics: Definition, process, and challenges. In: Kerren, A., Stasko, J.T., Fekete, J.-D., North, C. (eds.) Information Visualization. LNCS, vol. 4950, pp. 154–175. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  17. 17.
    van Wijk, J.J.: The value of visualization. In: IEEE Visualization, VIS 2005, pp. 79–86. IEEE (2005)Google Scholar
  18. 18.
    Holzinger, A.: Human-computer interaction and knowledge discovery (hci-kdd): What is the benefit of bringing those two fields to work together? In: Cuzzocrea, A., Kittl, C., Simos, D.E., Weippl, E., Xu, L. (eds.) CD-ARES 2013. LNCS, vol. 8127, pp. 319–328. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  19. 19.
    Holzinger, A., Jurisica, I.: Knowledge discovery and data mining in biomedical informatics: The future is in integrative, interactive machine learning solutions. In: Holzinger, A., Jurisica, I. (eds.) Interactive Knowledge Discovery and Data Mining in Biomedical Informatics: State-of-the-Art and Future Challenges. LNCS, vol. 8401, pp. 1–17. Springer, Heidelberg (2014)Google Scholar
  20. 20.
    Holzinger, A.: On knowledge discovery and interactive intelligent visualization of biomedical data - challenges in humancomputer interaction and biomedical informatics. In: DATA 2012, pp. 9–20. INSTICC (2012)Google Scholar
  21. 21.
    Fernald, G.H., Capriotti, E., Daneshjou, R., Karczewski, K.J., Altman, R.B.: Bioinformatics challenges for personalized medicine. Bioinformatics 27(13), 1741–1748 (2011)CrossRefGoogle Scholar
  22. 22.
    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., et al.: Visualizing biological datanow and in the future. Nature Methods 7, S2–S4 (2010)Google Scholar
  23. 23.
    Gehlenborg, N., O’Donoghue, S., Baliga, N., Goesmann, A., Hibbs, M., Kitano, H., Kohlbacher, O., Neuweger, H., Schneider, R., Tenenbaum, D., et al.: Visualization of omics data for systems biology. Nature Methods 7, S56–S68 (2010)Google Scholar
  24. 24.
    Nielsen, C.B., Cantor, M., Dubchak, I., Gordon, D., Wang, T.: Visualizing genomes: techniques and challenges. Nature Methods 7, S5–S15 (2010)Google Scholar
  25. 25.
    Munzner, T.: Visualization principles. Presented at VIZBI 2011: Workshop on Visualizing Biological Data (2011)Google Scholar
  26. 26.
    Hauser, H., Hagen, H., Theisel, H.: Topology-based methods in visualization (Mathematics+Visualization). Springer, Heidelberg (2007)CrossRefMATHGoogle Scholar
  27. 27.
    Pascucci, V., Tricoche, X., Hagen, H., Tierny, J.: Topological Methods in Data Analysis and Visualization: Theory, Algorithms, and Applications (Mathematics+Visualization). Springer, Heidelberg (2011)CrossRefMATHGoogle Scholar
  28. 28.
    Emmert-Streib, F., de Matos Simoes, R., Glazko, G., McDade, S., Haibe-Kains, B., Holzinger, A., Dehmer, M., Campbell, F.: Functional and genetic analysis of the colon cancer network. BMC Bioinformatics 15(suppl. 6), S6 (2014)Google Scholar
  29. 29.
    Olshen, L.B.J.F.R., Stone, C.J.: Classification and regression trees. Wadsworth International Group (1984)Google Scholar
  30. 30.
    Cohen, J., Cohen, P., West, S.G., Aiken, L.S.: Applied multiple regression/correlation analysis for the behavioral sciences. Lawrence Erlbaum (2003)Google Scholar
  31. 31.
    Crouser, R.J., Chang, R.: An affordance-based framework for human computation and human-computer collaboration. IEEE Transactions on Visualization and Computer Graphics 18(12), 2859–2868 (2012)CrossRefGoogle Scholar
  32. 32.
    Brehmer, M., Munzner, T.: A multi-level typology of abstract visualization tasks. IEEE Transactions on Visualization and Computer Graphics 19(12), 2376–2385 (2013)CrossRefGoogle Scholar
  33. 33.
    Kerren, A., Ebert, A., Meyer, J. (eds.): GI-Dagstuhl Research Seminar 2007. LNCS, vol. 4417. Springer, Heidelberg (2007)Google Scholar
  34. 34.
    Filzmoser, P., Hron, K., Reimann, C.: Principal component analysis for compositional data with outliers. Environmetrics 20(6), 621–632 (2009)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Novotný, M., Hauser, H.: Outlier-preserving focus+context visualization in parallel coordinates. IEEE Transactions on Visualization and Computer Graphics 12(5), 893–900 (2006)CrossRefGoogle Scholar
  36. 36.
    R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2013)Google Scholar
  37. 37.
    Martone, M.E., Tran, J., Wong, W.W., Sargis, J., Fong, L., Larson, S., Lamont, S.P., Gupta, A., Ellisman, M.H.: The cell centered database project: An update on building community resources for managing and sharing 3d imaging data. Journal of Structural Biology 161(3), 220–231 (2008)CrossRefGoogle Scholar
  38. 38.
    Jänicke, H., Böttinger, M., Scheuermann, G.: Brushing of attribute clouds for the visualization of multivariate data. IEEE Transactions on Visualization and Computer Graphics, 1459–1466 (2008)Google Scholar
  39. 39.
    Johansson, S., Johansson, J.: Interactive dimensionality reduction through user-defined combinations of quality metrics. IEEE Transactions on Visualization and Computer Graphics 15(6), 993–1000 (2009)CrossRefGoogle Scholar
  40. 40.
    Fernstad, S., Johansson, J., Adams, S., Shaw, J., Taylor, D.: Visual exploration of microbial populations. In: 2011 IEEE Symposium on Biological Data Visualization (BioVis), pp. 127–134 (2011)Google Scholar
  41. 41.
    Fuchs, R., Waser, J., Gröller, M.E.: Visual human+machine learning. IEEE TVCG 15(6), 1327–1334 (2009)Google Scholar
  42. 42.
    Oeltze, S., Doleisch, H., Hauser, H., Muigg, P., Preim, B.: Interactive visual analysis of perfusion data. IEEE Transactions on Visualization and Computer Graphics 13(6), 1392–1399 (2007)CrossRefGoogle Scholar
  43. 43.
    Carver, T., Harris, S.R., Berriman, M., Parkhill, J., McQuillan, J.A.: Artemis: An integrated platform for visualization and analysis of high-throughput sequence-based experimental data. Bioinformatics 28(4), 464–469 (2012)CrossRefGoogle Scholar
  44. 44.
    Franceschini, A., Szklarczyk, D., Frankild, S., Kuhn, M., Simonovic, M., Roth, A., Lin, J., Minguez, P., Bork, P., von Mering, C., et al.: String v9. 1: Protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Research 41(D1), D808–D815 (2013)Google Scholar
  45. 45.
    Perer, A., Shneiderman, B.: Integrating statistics and visualization for exploratory power: From long-term case studies to design guidelines. IEEE Computer Graphics and Applications 29(3), 39–51 (2009)CrossRefGoogle Scholar
  46. 46.
    Kuhn, R.M., Haussler, D., Kent, W.J.: The ucsc genome browser and associated tools. Briefings in Bioinformatics 14(2), 144–161 (2013)CrossRefGoogle Scholar
  47. 47.
    Thorvaldsdóttir, H., Robinson, J.T., Mesirov, J.P.: Integrative genomics viewer (igv): High-performance genomics data visualization and exploration. Briefings in Bioinformatics 14(2), 178–192 (2013)CrossRefGoogle Scholar
  48. 48.
    Yang, J., Hubball, D., Ward, M., Rundensteiner, E., Ribarsky, W.: Value and relation display: Interactive visual exploration of large data sets with hundreds of dimensions. IEEE Transactions on Visualization and Computer Graphics 13(3), 494–507 (2007)CrossRefGoogle Scholar
  49. 49.
    Kehrer, J., Filzmoser, P., Hauser, H.: Brushing moments in interactive visual analysis. Computer Graphics Forum 29(3), 813–822 (2010)CrossRefGoogle Scholar
  50. 50.
    Meyer, M., Munzner, T., DePace, A., Pfister, H.: Multeesum: A tool for comparative spatial and temporal gene expression data. IEEE Transactions on Visualization and Computer Graphics 16(6), 908–917 (2010)CrossRefGoogle Scholar
  51. 51.
    Nam, J., Mueller, K.: Tripadvisorn-d: A tourism-inspired high-dimensional space exploration framework with overview and detail. IEEE Transactions on Visualization and Computer Graphics 19(2), 291–305 (2013)CrossRefGoogle Scholar
  52. 52.
    Williams, M., Munzner, T.: Steerable, progressive multidimensional scaling. In: Proceedings of the IEEE Symposium on Information Visualization, pp. 57–64. IEEE Computer Society, Washington, DC (2004)CrossRefGoogle Scholar
  53. 53.
    Endert, A., Han, C., Maiti, D., House, L., North, C.: Observation-level interaction with statistical models for visual analytics. In: 2011 IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 121–130. IEEE (2011)Google Scholar
  54. 54.
    Ingram, S., Munzner, T., Irvine, V., Tory, M., Bergner, S., Möller, T.: Dimstiller: Workflows for dimensional analysis and reduction. In: 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST), pp. 3–10 (2010)Google Scholar
  55. 55.
    Endert, A., Bradel, L., North, C.: Beyond control panels: Direct manipulation for visual analytics. IEEE Computer Graphics and Applications 33(4), 6–13 (2013)CrossRefGoogle Scholar
  56. 56.
    Turkay, C., Filzmoser, P., Hauser, H.: Brushing dimensions – a dual visual analysis model for high-dimensional data. IEEE Transactions on Visualization and Computer Graphics 17(12), 2591–2599 (2011)CrossRefGoogle Scholar
  57. 57.
    Demšar, J., Leban, G., Zupan, B.: Freeviz - an intelligent multivariate visualization approach to explorative analysis of biomedical data. Journal of Biomedical Informatics 40(6), 661–671 (2007)CrossRefGoogle Scholar
  58. 58.
    Kosara, R., Bendix, F., Hauser, H.: Parallel sets: Interactive exploration and visual analysis of categorical data. IEEE Transactions on Visualization and Computer Graphics 12(4), 558–568 (2006)CrossRefGoogle Scholar
  59. 59.
    Telea, A., Auber, D.: Code flows: Visualizing structural evolution of source code. Computer Graphics Forum 27(3), 831–838 (2008)CrossRefGoogle Scholar
  60. 60.
    Lex, A., Streit, M., Schulz, H.J., Partl, C., Schmalstieg, D., Park, P.J., Gehlenborg, N.: StratomeX: Visual analysis of large-scale heterogeneous genomics data for cancer subtype characterization. Computer Graphics Forum (EuroVis 2012) 31(3), 1175–1184 (2012)CrossRefGoogle Scholar
  61. 61.
    Lex, A., Streit, M., Partl, C., Kashofer, K., Schmalstieg, D.: Comparative analysis of multidimensional, quantitative data. IEEE Transactions on Visualization and Computer Graphics (Proceedings Visualization / Information Visualization 2010) 16(6), 1027–1035 (2010)CrossRefGoogle Scholar
  62. 62.
    Partl, C., Kalkofen, D., Lex, A., Kashofer, K., Streit, M., Schmalstieg, D.: Enroute: Dynamic path extraction from biological pathway maps for in-depth experimental data analysis. In: 2012 IEEE Symposium on Biological Data Visualization (BioVis), pp. 107–114. IEEE (2012)Google Scholar
  63. 63.
    Turkay, C., Lex, A., Streit, M., Pfister, H., Hauser, H.: Characterizing cancer subtypes using dual analysis in caleydo stratomex. IEEE Computer Graphics and Applications 34(2), 38–47 (2014)CrossRefGoogle Scholar
  64. 64.
    May, T., Kohlhammer, J.: Towards closing the analysis gap: Visual generation of decision supporting schemes from raw data. In: Computer Graphics Forum, vol. 27, pp. 911–918. Wiley Online Library (2008)Google Scholar
  65. 65.
    May, T., Bannach, A., Davey, J., Ruppert, T., Kohlhammer, J.: Guiding feature subset selection with an interactive visualization. In: 2011 IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 111–120. IEEE (2011)Google Scholar
  66. 66.
    Younesy, H., Nielsen, C.B., Möller, T., Alder, O., Cullum, R., Lorincz, M.C., Karimi, M.M., Jones, S.J.: An interactive analysis and exploration tool for epigenomic data. In: Computer Graphics Forum, vol. 32, pp. 91–100. Wiley Online Library (2013)Google Scholar
  67. 67.
    Grottel, S., Reina, G., Vrabec, J., Ertl, T.: Visual verification and analysis of cluster detection for molecular dynamics. IEEE Transactions on Visualization and Computer Graphics 13(6), 1624–1631 (2007)CrossRefGoogle Scholar
  68. 68.
    Dietzsch, J., Gehlenborg, N., Nieselt, K.: Mayday-a microarray data analysis workbench. Bioinformatics 22(8), 1010–1012 (2006)CrossRefGoogle Scholar
  69. 69.
    Seo, J., Shneiderman, B.: Interactively exploring hierarchical clustering results. IEEE Computer 35(7), 80–86 (2002)CrossRefGoogle Scholar
  70. 70.
    Guo, Z., Ward, M.O., Rundensteiner, E.A.: Model space visualization for multivariate linear trend discovery. In: Proc. IEEE Symp. Visual Analytics Science and Technology VAST 2009, pp. 75–82 (2009)Google Scholar
  71. 71.
    Kandogan, E.: Just-in-time annotation of clusters, outliers, and trends in point-based data visualizations. In: 2012 IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 73–82. IEEE (2012)Google Scholar
  72. 72.
    Rinzivillo, S., Pedreschi, D., Nanni, M., Giannotti, F., Andrienko, N., Andrienko, G.: Visually driven analysis of movement data by progressive clustering. Information Visualization 7(3), 225–239 (2008)CrossRefGoogle Scholar
  73. 73.
    Schreck, T., Bernard, J., Tekusova, T., Kohlhammer, J.: Visual cluster analysis of trajectory data with interactive Kohonen Maps. In: IEEE Symposium on Visual Analytics Science and Technology, VAST 2008, pp. 3–10 (2008)Google Scholar
  74. 74.
    Rasmussen, M., Karypis, G.: gCLUTO–An Interactive Clustering, Visualization, and Analysis System., University of Minnesota, Department of Computer Science and Engineering, CSE. Technical report, UMN Technical Report: TR (2004)Google Scholar
  75. 75.
    Ahmed, Z., Weaver, C.: An Adaptive Parameter Space-Filling Algorithm for Highly Interactive Cluster Exploration. In: Procedings of IEEE Symposium on Visual Analytics Science and Technology, VAST (2012)Google Scholar
  76. 76.
    Rubel, O., Weber, G., Huang, M.Y., Bethel, E., Biggin, M., Fowlkes, C., Luengo Hendriks, C., Keranen, S., Eisen, M., Knowles, D., Malik, J., Hagen, H., Hamann, B.: Integrating data clustering and visualization for the analysis of 3D gene expression data. IEEE/ACM Transactions on Computational Biology and Bioinformatics 7(1), 64–79 (2010)CrossRefGoogle Scholar
  77. 77.
    Turkay, C., Parulek, J., Reuter, N., Hauser, H.: Interactive visual analysis of temporal cluster structures. Computer Graphics Forum 30(3), 711–720 (2011)CrossRefGoogle Scholar
  78. 78.
    Parulek, J., Turkay, C., Reuter, N., Viola, I.: Visual cavity analysis in molecular simulations. BMC Bioinformatics 14(19), 1–15 (2013)Google Scholar
  79. 79.
    Turkay, C., Parulek, J., Reuter, N., Hauser, H.: Integrating cluster formation and cluster evaluation in interactive visual analysis. In: Proceedings of the 27th Spring Conference on Computer Graphics, pp. 77–86. ACM (2011)Google Scholar
  80. 80.
    Choo, J., Lee, H., Kihm, J., Park, H.: ivisclassifier: An interactive visual analytics system for classification based on supervised dimension reduction. In: 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST), pp. 27–34. IEEE (2010)Google Scholar
  81. 81.
    Krzywinski, M., Schein, J., Birol, İ., Connors, J., Gascoyne, R., Horsman, D., Jones, S.J., Marra, M.A.: Circos: An information aesthetic for comparative genomics. Genome Research 19(9), 1639–1645 (2009)CrossRefGoogle Scholar
  82. 82.
    Karr, J.R., Sanghvi, J.C., Macklin, D.N., Gutschow, M.V., Jacobs, J.M., Bolival Jr., B., Assad-Garcia, N., Glass, J.I., Covert, M.W.: A whole-cell computational model predicts phenotype from genotype. Cell 150(2), 389–401 (2012)CrossRefGoogle Scholar
  83. 83.
    Meyer, M., Munzner, T., Pfister, H.: Mizbee: A multiscale synteny browser. IEEE Transactions on Visualization and Computer Graphics 15(6), 897–904 (2009)CrossRefGoogle Scholar
  84. 84.
    Piringer, H., Berger, W., Krasser, J.: Hypermoval: Interactive visual validation of regression models for real-time simulation. In: Proceedings of the 12th Eurographics / IEEE - VGTC Conference on Visualization. EuroVis 2010, pp. 983–992. Eurographics Association, Aire-la-Ville (2010)Google Scholar
  85. 85.
    Muhlbacher, T., Piringer, H.: A partition-based framework for building and validating regression models. IEEE Transactions on Visualization and Computer Graphics 19(12), 1962–1971 (2013)CrossRefGoogle Scholar
  86. 86.
    Booshehrian, M., Möller, T., Peterman, R.M., Munzner, T.: Vismon: Facilitating analysis of trade-offs, uncertainty, and sensitivity in fisheries management decision making. In: Computer Graphics Forum, vol. 31, pp. 1235–1244. Wiley Online Library (2012)Google Scholar
  87. 87.
    Meyer, M., Wong, B., Styczynski, M., Munzner, T., Pfister, H.: Pathline: A tool for comparative functional genomics. In: Computer Graphics Forum, vol. 29, pp. 1043–1052. Wiley Online Library (2010)Google Scholar
  88. 88.
    Elmqvist, N., Dragicevic, P., Fekete, J.: Rolling the dice: Multidimensional visual exploration using scatterplot matrix navigation. IEEE Transactions on Visualization and Computer Graphics 14(6), 1539–1148 (2008)Google Scholar
  89. 89.
    Yang, J., Ward, M.O., Rundensteiner, E.A., Huang, S.: Visual hierarchical dimension reduction for exploration of high dimensional datasets. In: VISSYM 2003: Proceedings of the Symposium on Data Visualisation 2003, pp. 19–28 (2003)Google Scholar
  90. 90.
    Berger, W., Piringer, H., Filzmoser, P., Gröller, E.: Uncertainty-aware exploration of continuous parameter spaces using multivariate prediction. Computer Graphics Forum 30(3), 911–920 (2011)CrossRefGoogle Scholar
  91. 91.
    Malik, A., Maciejewski, R., Elmqvist, N., Jang, Y., Ebert, D.S., Huang, W.: A correlative analysis process in a visual analytics environment. In: 2012 IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 33–42. IEEE (2012)Google Scholar
  92. 92.
    Turkay, C., Lundervold, A., Lundervold, A., Hauser, H.: Representative factor generation for the interactive visual analysis of high-dimensional data. IEEE Transactions on Visualization and Computer Graphics 18(12), 2621–2630 (2012)CrossRefGoogle Scholar
  93. 93.
    Mirkin, B.: Core Concepts in Data Analysis: Summarization, Correlation and Visualization. Springer (2011)Google Scholar
  94. 94.
    Procter, J.B., Thompson, J., Letunic, I., Creevey, C., Jossinet, F., Barton, G.J.: Visualization of multiple alignments, phylogenies and gene family evolution. Nature Methods 7, S16–S25 (2010)Google Scholar
  95. 95.
    Otasek, D., Pastrello, C., Holzinger, A., Jurisica, I.: Visual Data Mining: Effective Exploration of the Biological Universe. In: Holzinger, A., Jurisica, I. (eds.) Knowledge Discovery and Data Mining. LNCS, vol. 8401, pp. 19–34. Springer, Heidelberg (2014)Google Scholar
  96. 96.
    Mueller, H., Reihs, R., Zatloukal, K., Holzinger, A.: Analysis of biomedical data with multilevel glyphs. BMC Bioinformatics 15(suppl. 6), S5 (2014)Google Scholar
  97. 97.
    Tan, P., Steinbach, M., Kumar, V.: Introduction to data mining. Pearson Addison Wesley, Boston (2006)Google Scholar
  98. 98.
    van den Elzen, S., van Wijk, J.J.: Baobabview: Interactive construction and analysis of decision trees. In: 2011 IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 151–160. IEEE (2011)Google Scholar
  99. 99.
    Hair, J., Anderson, R.: Multivariate data analysis. Prentice Hall (2010)Google Scholar
  100. 100.
    Secrier, M., Schneider, R.: Visualizing time-related data in biology, a review. Briefings in Bioinformatics, bbt021 (2013)Google Scholar
  101. 101.
    Chen, C.: Top 10 unsolved information visualization problems. IEEE Computer Graphics and Applications 25(4), 12–16 (2005)CrossRefGoogle Scholar
  102. 102.
    Jeanquartier, F., Holzinger, A.: On Visual Analytics And Evaluation In Cell Physiology: A Case Study. In: Cuzzocrea, A., Kittl, C., Simos, D.E., Weippl, E., Xu, L. (eds.) CD-ARES 2013. LNCS, vol. 8127, pp. 495–502. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  103. 103.
    Holzinger, A.: Usability engineering methods for software developers. Communications of the ACM 48(1), 71–74 (2005)CrossRefGoogle Scholar
  104. 104.
    Kehrer, J., Hauser, H.: Visualization and visual analysis of multifaceted scientific data: A survey. IEEE Transactions on Visualization and Computer Graphics 19(3), 495–513 (2013)CrossRefGoogle Scholar
  105. 105.
    Matkovic, K., Gracanin, D., Jelovic, M., Hauser, H.: Interactive visual steering-rapid visual prototyping of a common rail injection system. IEEE Transactions on Visualization and Computer Graphics 14(6), 1699–1706 (2008)CrossRefGoogle Scholar
  106. 106.
    Beale, R.: Supporting serendipity: Using ambient intelligence to augment user exploration for data mining and web browsing. International Journal of Human-Computer Studies 65(5), 421–433 (2007)CrossRefGoogle Scholar
  107. 107.
    Holzinger, A., Kickmeier-Rust, M., Albert, D.: Dynamic media in computer science education; content complexity and learning performance: Is less more? Educational Technology & Society 11(1), 279–290 (2008)Google Scholar
  108. 108.
    Ceglar, A., Roddick, J.F., Calder, P.: Guiding knowledge discovery through interactive data mining. Managing Data Mining Technologies in Organizations: Techniques and Applications, 45–87 (2003)Google Scholar
  109. 109.
    Chau, D.H., Myers, B., Faulring, A.: What to do when search fails: finding information by association. In: Proceeding of the Twenty-Sixth Annual SIGCHI Conference on Human Factors in Computing Systems, pp. 999–1008. ACM (2008)Google Scholar
  110. 110.
    Olshausen, B.A., Anderson, C.H., Vanessen, D.C.: A neurobiological model of visual-attention and invariant pattern-recognition based on dynamic routing of information. Journal of Neuroscience 13(11), 4700–4719 (1993)Google Scholar
  111. 111.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: A survey. ACM Computing Surveys (CSUR) 41(3), 15 (2009)CrossRefGoogle Scholar
  112. 112.
    Edelsbrunner, H., Harer, J.L.: Computational Topology: An Introduction. American Mathematical Society, Providence (2010)Google Scholar
  113. 113.
    Holzinger, A.: On topological data mining. In: Holzinger, A., Jurisica, I. (eds.) Knowledge Discovery and Data Mining. LNCS, vol. 8401, pp. 331–356. Springer, Heidelberg (2014)Google Scholar
  114. 114.
    Bremer, P.T., Pascucci, V., Hamann, B.: Maximizing Adaptivity in Hierarchical Topological Models Using Cancellation Trees, pp. 1–18. Springer (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Cagatay Turkay
    • 1
  • Fleur Jeanquartier
    • 2
  • Andreas Holzinger
    • 2
  • Helwig Hauser
    • 3
  1. 1.giCentre, Department of Computer ScienceCity UniversityLondonUK
  2. 2.Research Unit HCI, Institute for Medical Informatics, Statistics and DocumentationMedical University GrazGrazAustria
  3. 3.Visualization Group, Department of InformaticsUniversity of BergenNorway

Personalised recommendations