Novel method of identifying DNA methylation fingerprint of acute myeloid leukaemia

Agnieszka Cecotka; Joanna Polanska

doi:10.1007/978-3-319-60816-7_23

Novel method of identifying DNA methylation fingerprint of acute myeloid leukaemia

Agnieszka Cecotka
, Joanna Polanska

Department of Data Science and Engineering (RAU/RAU4)

Silesian University of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	11th International Conference on Practical Applications of Computational Biology and Bioinformatics, 2017
Editors	Miguel Rocha, Juan F. De Paz, Tiago Pinto, Florentino Fdez-Riverola, Mohd Saberi Mohamad
Publisher	Springer Verlag
Pages	189-196
Number of pages	8
ISBN (Print)	9783319608150
DOIs	https://doi.org/10.1007/978-3-319-60816-7_23
Publication status	Published - 2017
Event	11th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2017 - Porto, Portugal Duration: 21 Jun 2017 → 23 Jun 2017

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	616
ISSN (Print)	2194-5357

Conference

Conference	11th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2017
Country/Territory	Portugal
City	Porto
Period	21/06/17 → 23/06/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Acute Myeloid Leukaemia
DNA methylation
Epigenetics
Gaussian mixture model
Mathematical modelling
Robust estimator

ASJC Scopus subject areas

Control and Systems Engineering
General Computer Science

Access to Document

10.1007/978-3-319-60816-7_23

Cite this

Cecotka, A., & Polanska, J. (2017). Novel method of identifying DNA methylation fingerprint of acute myeloid leukaemia. In M. Rocha, J. F. De Paz, T. Pinto, F. Fdez-Riverola, & M. S. Mohamad (Eds.), 11th International Conference on Practical Applications of Computational Biology and Bioinformatics, 2017 (pp. 189-196). (Advances in Intelligent Systems and Computing; Vol. 616). Springer Verlag. https://doi.org/10.1007/978-3-319-60816-7_23

Cecotka, Agnieszka ; Polanska, Joanna. / Novel method of identifying DNA methylation fingerprint of acute myeloid leukaemia. 11th International Conference on Practical Applications of Computational Biology and Bioinformatics, 2017. editor / Miguel Rocha ; Juan F. De Paz ; Tiago Pinto ; Florentino Fdez-Riverola ; Mohd Saberi Mohamad. Springer Verlag, 2017. pp. 189-196 (Advances in Intelligent Systems and Computing).

@inproceedings{1fe244d0a96543df8a97d0207baa0a37,

title = "Novel method of identifying DNA methylation fingerprint of acute myeloid leukaemia",

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.",

keywords = "Acute Myeloid Leukaemia, DNA methylation, Epigenetics, Gaussian mixture model, Mathematical modelling, Robust estimator",

author = "Agnieszka Cecotka and Joanna Polanska",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 11th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2017 ; Conference date: 21-06-2017 Through 23-06-2017",

year = "2017",

doi = "10.1007/978-3-319-60816-7\_23",

language = "English",

isbn = "9783319608150",

series = "Advances in Intelligent Systems and Computing",

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booktitle = "11th International Conference on Practical Applications of Computational Biology and Bioinformatics, 2017",

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Cecotka, A & Polanska, J 2017, Novel method of identifying DNA methylation fingerprint of acute myeloid leukaemia. in M Rocha, JF De Paz, T Pinto, F Fdez-Riverola & MS Mohamad (eds), 11th International Conference on Practical Applications of Computational Biology and Bioinformatics, 2017. Advances in Intelligent Systems and Computing, vol. 616, Springer Verlag, pp. 189-196, 11th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2017, Porto, Portugal, 21/06/17. https://doi.org/10.1007/978-3-319-60816-7_23

Novel method of identifying DNA methylation fingerprint of acute myeloid leukaemia. / Cecotka, Agnieszka; Polanska, Joanna.
11th International Conference on Practical Applications of Computational Biology and Bioinformatics, 2017. ed. / Miguel Rocha; Juan F. De Paz; Tiago Pinto; Florentino Fdez-Riverola; Mohd Saberi Mohamad. Springer Verlag, 2017. p. 189-196 (Advances in Intelligent Systems and Computing; Vol. 616).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Novel method of identifying DNA methylation fingerprint of acute myeloid leukaemia

AU - Cecotka, Agnieszka

AU - Polanska, Joanna

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - Acute Myeloid Leukaemia

KW - DNA methylation

KW - Epigenetics

KW - Gaussian mixture model

KW - Mathematical modelling

KW - Robust estimator

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DO - 10.1007/978-3-319-60816-7_23

M3 - Conference contribution

AN - SCOPUS:85025141421

SN - 9783319608150

T3 - Advances in Intelligent Systems and Computing

SP - 189

EP - 196

BT - 11th International Conference on Practical Applications of Computational Biology and Bioinformatics, 2017

A2 - Rocha, Miguel

A2 - De Paz, Juan F.

A2 - Pinto, Tiago

A2 - Fdez-Riverola, Florentino

A2 - Mohamad, Mohd Saberi

PB - Springer Verlag

T2 - 11th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2017

Y2 - 21 June 2017 through 23 June 2017

ER -

Cecotka A, Polanska J. Novel method of identifying DNA methylation fingerprint of acute myeloid leukaemia. In Rocha M, De Paz JF, Pinto T, Fdez-Riverola F, Mohamad MS, editors, 11th International Conference on Practical Applications of Computational Biology and Bioinformatics, 2017. Springer Verlag. 2017. p. 189-196. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-319-60816-7_23

Novel method of identifying DNA methylation fingerprint of acute myeloid leukaemia

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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