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Computational Methods for Integration of Biological Data

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Personalized Medicine

Part of the book series: Europeanization and Globalization ((EAG,volume 2))

Abstract

As we keep accumulating large sets of diverse biological data, there is a need for efficient extraction of biological knowledge from the data. Developing computational tools for efficient integration of biological data, obtained from diverse experiments, has recently gained attention. Previous computational tools are mainly designed to analyze only one particular type of the biological data. Data of one type provide an incomplete and often obscure picture of cellular functioning. Analysis of these data separately can only partially address important biological questions, such as emergence of diseases and the development of novel diagnostic and therapeutic approaches. Therefore, a key strategy for deeper understanding of the functioning of a cell and better understanding of the molecular bases of human diseases is data integration. Here, we classify current integrative approaches and review their applications in addressing fundamental biological questions that can increase our understanding of a biological system.

Vladimir Gligorijević, Ph.D. Department of Computing, Imperial College London, UK.

Professor Nataša Pržulj, Ph.D., Department of Computing, University College London, UK.

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Notes

  1. 1.

    Ito et al. (2000), Uetz et al. (2000), Giot et al. (2003), Li et al. (2004), Stelzl et al. (2005), Simonis et al. (2009), and Consortium AIM (2011).

  2. 2.

    Gavin et al. (2006) and Krogan et al. (2006).

  3. 3.

    Dahlquist et al. (2002) and Quackenbush (2001).

  4. 4.

    Marioni et al. (2008), Mortazavi et al. (2008), and Wang et al. (2009).

  5. 5.

    Wang et al. (2003) and Smith et al. (2006).

  6. 6.

    Vidal et al. (2011).

  7. 7.

    Pržulj (2011), Sarajlić and Pržulj (2014), Aittokallio and Schwikowski (2006).

  8. 8.

    Mccarthy et al. (2008), Sladek et al. (2007), and The Wellcome Trust Case Control Consortium (2007).

  9. 9.

    Ashburner et al. (2000).

  10. 10.

    Schriml et al. (2012).

  11. 11.

    Lu et al. (2005).

  12. 12.

    Wang et al. (2013).

  13. 13.

    Ma et al. (2013).

  14. 14.

    Zhang et al. (2011).

  15. 15.

    Hamburg and Collins (2010).

  16. 16.

    Dudley and Karczewski (2013).

  17. 17.

    Evers et al. (2012).

  18. 18.

    Gomez-Cabrero et al. (2014).

  19. 19.

    Ashburner et al. (2000).

  20. 20.

    Whisstock and Lesk (2003).

  21. 21.

    Ashburn and Thor (2004).

  22. 22.

    Hurle et al. (2013).

  23. 23.

    Wu et al. (2013).

  24. 24.

    Quinn et al. (2013).

  25. 25.

    Trenkwalder et al. (2004).

  26. 26.

    Hurle et al. (2013).

  27. 27.

    Ding et al. (2013) and Wang et al. (2013).

  28. 28.

    Wang et al. (2013), Yamanishi et al. (2008), Napolitano et al. (2013), and Huang et al. (2013).

  29. 29.

    Schriml et al. (2012) and Osborne et al. (2009).

  30. 30.

    Gatza et al. (2010).

  31. 31.

    Lee et al. (2008).

  32. 32.

    Žitnik et al. (2013).

  33. 33.

    Bromberg (2013).

  34. 34.

    Goh et al. (2007).

  35. 35.

    Köhler et al. (2008).

  36. 36.

    Vanunu et al. (2010).

  37. 37.

    Bebek et al. (2012).

  38. 38.

    Chen et al. (2013).

  39. 39.

    Linghu et al. (2009).

  40. 40.

    West (2000).

  41. 41.

    Albert (2005).

  42. 42.

    Barabasi and Oltvai (2004), Higham et al. (2008), and Pržulj et al. (2004).

  43. 43.

    West (2000).

  44. 44.

    Newman (2010) and Pržulj et al. (2004).

  45. 45.

    Barabasi and Oltvai (2004), Pržulj (2011), and Stelzl et al. (2005).

  46. 46.

    Consortium AIM (2011), Dreze et al. (2010), Giot et al. (2003), Ito et al. (2000), Li et al. (2004), Stelzl et al. (2005), and Uetz et al. (2000).

  47. 47.

    Gavin et al. (2006) and Krogan et al. (2006).

  48. 48.

    Chatr-Aryamontri et al. (2013).

  49. 49.

    Franceschini et al. (2013).

  50. 50.

    Keshava Prasad et al. (2009).

  51. 51.

    Schuster et al. (2000) and Vidal et al. (2011).

  52. 52.

    Kanehisa et al. (2012) and Zhou (2013).

  53. 53.

    Luo et al. (2007).

  54. 54.

    Prieto et al. (2008).

  55. 55.

    Ziv et al. (2003).

  56. 56.

    De Smet et al. (2002).

  57. 57.

    Luo et al. (2007).

  58. 58.

    Barrett et al. (2007).

  59. 59.

    Parkinson et al. (2005).

  60. 60.

    Hubble et al. (2009).

  61. 61.

    Costanzo et al. (2010), Mani et al. (2008), and Vidal et al. (2011).

  62. 62.

    Mani et al. (2008).

  63. 63.

    Vidal et al. (2011).

  64. 64.

    Chatr-Aryamontri et al. (2013).

  65. 65.

    Eungdamrong and Iyengar (2004).

  66. 66.

    Eungdamrong and Iyengar (2004) and Soyer et al. (2006).

  67. 67.

    Žitnik et al. (2013).

  68. 68.

    Schacherer et al. (2001).

  69. 69.

    Hamosh et al. (2005).

  70. 70.

    Davis et al. (2013).

  71. 71.

    Osborne et al. (2009).

  72. 72.

    Yildirim et al. (2007).

  73. 73.

    Daminelli et al. (2012), Wu et al. (2013), and Yamanishi et al. (2008).

  74. 74.

    Wishart et al. (2008).

  75. 75.

    Kanehisa et al. (2006).

  76. 76.

    Gunther et al. (2008).

  77. 77.

    Schomburg et al. (2013).

  78. 78.

    Yildirim et al. (2007).

  79. 79.

    Ding et al. (2013).

  80. 80.

    Wishart et al. (2008).

  81. 81.

    Kanehisa et al. (2006).

  82. 82.

    Kuhn et al. (2010).

  83. 83.

    Zhang et al. (2013).

  84. 84.

    Lamb et al. (2006).

  85. 85.

    Ashburner et al. (2000).

  86. 86.

    Schriml et al. (2012).

  87. 87.

    Ashburner et al. (2000).

  88. 88.

    Nelson et al. (2004).

  89. 89.

    Ayme et al. (2010).

  90. 90.

    Sioutos et al. (2007).

  91. 91.

    Cornet and de Keizer (2008).

  92. 92.

    Hamosh et al. (2005).

  93. 93.

    |V | stands for the number of elements in set V.

  94. 94.

    West (2000).

  95. 95.

    Semi-supervised learning is a class of machine learning techniques that uses small amounts of labeled data (prior information about data) for training an algorithm in performing a specific task. For example, in clustering tasks, traditional clustering algorithms use only unlabeled data to generate clusters, whereas semi-supervised clustering algorithms use prior information about the data to improve the clustering results. For instance, when performing clustering of genes, the supervision is generally given as pairwise constraints that guide the clustering process; such constrains are naturally given as molecular networks, that is, two connected genes in a molecular network are considered to belong to the same cluster.

  96. 96.

    Zhu et al. (2005).

  97. 97.

    Gligorijević et al. (2014).

  98. 98.

    Milenković and Pržulj (2008).

  99. 99.

    Ge et al. (2003).

  100. 100.

    Mani et al. (2008).

  101. 101.

    Žitnik et al. (2013).

  102. 102.

    Gligorijević et al. (2014).

  103. 103.

    de Silva et al. (2006) and Wodak et al. (2009).

  104. 104.

    Yu et al. (2011).

  105. 105.

    Ben-Gal (2007).

  106. 106.

    Yu et al. (2011).

  107. 107.

    Cooper and Herskovits (1992).

  108. 108.

    Dempster et al. (1977).

  109. 109.

    Ben-Gal (2007).

  110. 110.

    Fawcett (2006).

  111. 111.

    Nariai et al. (2007).

  112. 112.

    Linghu et al. (2009).

  113. 113.

    Schadt et al. (2012).

  114. 114.

    A cis eQTL is an eQTL that is located near the expressed gene.

  115. 115.

    Yu et al. (2011).

  116. 116.

    Boyd and Vandenberghe (2004).

  117. 117.

    Scholkopf and Smola (2001).

  118. 118.

    Lanckriet et al. (2004a, b).

  119. 119.

    Žitnik et al. (2013).

  120. 120.

    Wang et al. (2011).

  121. 121.

    Lee and Seung (1999).

  122. 122.

    Ding et al. (2006).

  123. 123.

    Wang et al. (2008).

  124. 124.

    Žitnik et al. (2013).

  125. 125.

    Koren et al. (2009).

  126. 126.

    Gligorijević et al. (2014).

  127. 127.

    Hwang et al. (2012).

  128. 128.

    Natarajan and Dhillon (2014).

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Acknowledgments

This work was supported by the European Research Council (ERC) Starting Independent Researcher Grant 278212, the National Science Foundation (NSF) Cyber-Enabled Discovery and Innovation (CDI) OIA-1028394, the Serbian Ministry of Education and Science Project III44006, and ARRS project J1-5454.

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  1. Department of Computing, Imperial College London, London, UK

    Vladimir Gligorijević Ph.D. & Nataša Pržulj Ph.D.

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  1. Vladimir Gligorijević Ph.D.
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  3. Department of Biotechnology, University of Rijeka , Rijeka, Croatia

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  4. University of Applied Sciences, Hochschule für öffentliche Verwaltung und Finanzen Ludwigsburg, Ludwigsburg, Germany

    Gerald G. Sander

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Gligorijević, V., Pržulj, N. (2016). Computational Methods for Integration of Biological Data. In: Bodiroga-Vukobrat, N., Rukavina, D., Pavelić, K., Sander, G. (eds) Personalized Medicine. Europeanization and Globalization, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-319-39349-0_8

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