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Novel Method of Identifying DNA Methylation Fingerprint of Acute Myeloid Leukaemia

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11th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 616))

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Abstract

Finding new statistical approaches to high throughput data analysis is a very hot topic nowadays. Such a data needs dedicated methods and algorithms of analysis due to huge number of features, but often also due to a small number of samples. Methylation data are also very special, because of dependencies between features and their neighbourhood. There is a need to find a novel, data driven algorithm for these data owing to big variety of distributions data sets. Purpose of this method is detection of regions with different levels of demethylation. From the biological point of view, the most important genome regions are TSS (transcription start site) regions. Hypermethylation of these part of a gene leads to repression and thus stop the gene expression. This phenomenon often happens in cancer disease and impairs a number of molecular processes in the cell. The proposed algorithm is performed for AML patients data in comparison to healthy control. By combination of statistics methods and mathematical modelling together, it enables detection of demethylated regions or DNA and their classification as low, medium or high demethylated.

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Acknowledgements

This work was financed by SUT grant no. BKM/506/RAU1/2016/t.29 (AC) and SUT grant no. 02/010/BK_16/3015 (JP). All the calculations were carried out using infrastructure funded by GeCONiI project (POIG.02.03.01-24-099/13).

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Authors and Affiliations

  1. Data Mining Group, Faculty of Automatic Control, Electronics and Computer Science, Institute of Automatic Control, Silesian University of Technology, ul. Akademicka 16, 44-100, Gliwice, Poland

    Agnieszka Cecotka & Joanna Polanska

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  1. Agnieszka Cecotka
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  2. Joanna Polanska
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Correspondence to Agnieszka Cecotka .

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Editors and Affiliations

  1. Escuela Superior de Ingeniería Informática, Universidad de Vigo , Ourense, Spain

    Florentino Fdez-Riverola

  2. Faculty of Computing, Universiti Teknologi Malaysia , Johor, Johor, Malaysia

    Mohd Saberi Mohamad

  3. Department de Informática, Universidade do Minho , Braga, Portugal

    Miguel Rocha

  4. Departamento de Informática y Automática, Universidad de Salamanca , Salamanca, Spain

    Juan F. De Paz

  5. Departamento de Informática y Automática, Universidad de Salamanca , Salamanca, Spain

    Tiago Pinto

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Cecotka, A., Polanska, J. (2017). Novel Method of Identifying DNA Methylation Fingerprint of Acute Myeloid Leukaemia. In: Fdez-Riverola, F., Mohamad, M., Rocha, M., De Paz, J., Pinto, T. (eds) 11th International Conference on Practical Applications of Computational Biology & Bioinformatics. PACBB 2017. Advances in Intelligent Systems and Computing, vol 616. Springer, Cham. https://doi.org/10.1007/978-3-319-60816-7_23

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  • DOI: https://doi.org/10.1007/978-3-319-60816-7_23

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