Graph Centrality Based Prediction of Cancer Genes

  • Holger WeishauptEmail author
  • Patrik Johansson
  • Christopher Engström
  • Sven Nelander
  • Sergei Silvestrov
  • Fredrik J. Swartling
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 179)


Current cancer therapies including surgery, radiotherapy and chemotherapy are often plagued by high failure rates. Designing more targeted and personalized treatment strategies requires a detailed understanding of druggable tumor driver genes. As a consequence, the detection of cancer driver genes has evolved to a critical scientific field integrating both high-throughput experimental screens as well as computational and statistical strategies. Among such approaches, network based prediction tools have recently been accentuated and received major focus due to their potential to model various aspects of the role of cancer genes in a biological system. In this chapter, we focus on how graph centralities obtained from biological networks have been used to predict cancer genes. Specifically, we start by discussing the current problems in cancer therapy and the reasoning behind using network based cancer gene prediction, followed by an outline of biological networks, their generation and properties. Finally, we review major concepts, recent results as well as future challenges regarding the use of graph centralities in cancer gene prediction.


Biological networks Graph centrality Disease genes Gene prioritization 



This work was supported by grants from the Swedish Childhood Cancer Foundation.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Holger Weishaupt
    • 1
    Email author
  • Patrik Johansson
    • 1
  • Christopher Engström
    • 2
  • Sven Nelander
    • 3
  • Sergei Silvestrov
    • 2
  • Fredrik J. Swartling
    • 3
  1. 1.Department of Immunology, Genetics and Pathology, Science for Life LaboratoryUppsala UniversityUppsalaSweden
  2. 2.Division of Applied Mathematics, School of Education, Culture and CommunicationMälardalen UniversityVästeråsSweden
  3. 3.Department of Immunology, Genetics and PathologyUppsala UniversityUppsalaSweden

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