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Graph Centrality Based Prediction of Cancer Genes

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Engineering Mathematics II

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 179))

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Abstract

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.

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Notes

  1. 1.

    A comprehensive list of centralities can be found in the CentiServer (http://www.centiserver.org/) [72].

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Acknowledgements

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

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Authors and Affiliations

  1. Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Dag Hammarskjöldsväg 20, 751 85, Uppsala, Sweden

    Holger Weishaupt & Patrik Johansson

  2. Division of Applied Mathematics, School of Education, Culture and Communication, Mälardalen University, Box 883, 721 23, Västerås, Sweden

    Christopher Engström & Sergei Silvestrov

  3. Department of Immunology, Genetics and Pathology, Uppsala University, Dag Hammarskjöldsväg 20, 751 85, Uppsala, Sweden

    Sven Nelander & Fredrik J. Swartling

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  1. Holger Weishaupt
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Correspondence to Holger Weishaupt .

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  1. Division of Applied Mathematics, School of Education, Culture and Communication, Mälardalen University,, Västerås, Sweden

    Sergei Silvestrov

  2. Division of Applied Mathematics,, School of Education, Culture and Communication, Mälardalen University, Västerås, Sweden

    Milica Rančić

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Weishaupt, H., Johansson, P., Engström, C., Nelander, S., Silvestrov, S., Swartling, F.J. (2016). Graph Centrality Based Prediction of Cancer Genes. In: Silvestrov, S., Rančić, M. (eds) Engineering Mathematics II. Springer Proceedings in Mathematics & Statistics, vol 179. Springer, Cham. https://doi.org/10.1007/978-3-319-42105-6_13

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