Deep data analysis of a large microarray collection for leukemia biomarker identification

Wojciech Labaj; Anna Papiez; Joanna Polanska; Andrzej Polanski

doi:10.1007/978-3-319-40126-3_8

Deep data analysis of a large microarray collection for leukemia biomarker identification

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

Abstract

Nowadays, statistical design of experiments allows for planning of complex studies while maintaining control over technical bias. In this study, the equal importance of performing tailored preprocessing, such as batch effect adjustment and adaptive signal filtration, is demonstrated in order to enhance quality of the results. This approach is assessed on a large set of data on acute and chronic leukemia cases. It is shown, both through statistical analysis and literature research, that drawing attention toward data preprocessing is worthwhile, as it produces meaningful original biological conclusions. Specifically in this case, it entailed the revealing of four candidate leukemia biomarkers for further investigation of their significance.

Original language	English
Title of host publication	10th International Conference on Practical Applications of Computational Biology and Bioinformatics
Editors	Florentino Fdez-Riverola, Juan F. De Paz, Miguel P. Rocha, Francisco J. Domínguez Mayo, Mohd Saberi Mohamad
Publisher	Springer Verlag
Pages	71-79
Number of pages	9
ISBN (Print)	9783319401256
DOIs	https://doi.org/10.1007/978-3-319-40126-3_8
Publication status	Published - 2016
Event	International Conference on Practical Applications of Computational Biology and Bioinformatics PACBB, 2016 - Sevilla, Spain Duration: 1 Jun 2016 → 3 Jun 2016

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	477
ISSN (Print)	2194-5357

Conference

Conference	International Conference on Practical Applications of Computational Biology and Bioinformatics PACBB, 2016
Country/Territory	Spain
City	Sevilla
Period	1/06/16 → 3/06/16

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Batch effect
Biomarker identification
Gene expression
High-throughput
Leukemia

ASJC Scopus subject areas

Control and Systems Engineering
General Computer Science

Access to Document

10.1007/978-3-319-40126-3_8

Cite this

Labaj, W., Papiez, A., Polanska, J., & Polanski, A. (2016). Deep data analysis of a large microarray collection for leukemia biomarker identification. In F. Fdez-Riverola, J. F. De Paz, M. P. Rocha, F. J. D. Mayo, & M. S. Mohamad (Eds.), 10th International Conference on Practical Applications of Computational Biology and Bioinformatics (pp. 71-79). (Advances in Intelligent Systems and Computing; Vol. 477). Springer Verlag. https://doi.org/10.1007/978-3-319-40126-3_8

Labaj, Wojciech ; Papiez, Anna ; Polanska, Joanna et al. / Deep data analysis of a large microarray collection for leukemia biomarker identification. 10th International Conference on Practical Applications of Computational Biology and Bioinformatics. editor / Florentino Fdez-Riverola ; Juan F. De Paz ; Miguel P. Rocha ; Francisco J. Domínguez Mayo ; Mohd Saberi Mohamad. Springer Verlag, 2016. pp. 71-79 (Advances in Intelligent Systems and Computing).

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title = "Deep data analysis of a large microarray collection for leukemia biomarker identification",

abstract = "Nowadays, statistical design of experiments allows for planning of complex studies while maintaining control over technical bias. In this study, the equal importance of performing tailored preprocessing, such as batch effect adjustment and adaptive signal filtration, is demonstrated in order to enhance quality of the results. This approach is assessed on a large set of data on acute and chronic leukemia cases. It is shown, both through statistical analysis and literature research, that drawing attention toward data preprocessing is worthwhile, as it produces meaningful original biological conclusions. Specifically in this case, it entailed the revealing of four candidate leukemia biomarkers for further investigation of their significance.",

keywords = "Batch effect, Biomarker identification, Gene expression, High-throughput, Leukemia",

author = "Wojciech Labaj and Anna Papiez and Joanna Polanska and Andrzej Polanski",

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year = "2016",

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editor = "Florentino Fdez-Riverola and \{De Paz\}, \{Juan F.\} and Rocha, \{Miguel P.\} and Mayo, \{Francisco J. Dom{\'i}nguez\} and Mohamad, \{Mohd Saberi\}",

booktitle = "10th International Conference on Practical Applications of Computational Biology and Bioinformatics",

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Labaj, W , Papiez, A , Polanska, J & Polanski, A 2016, Deep data analysis of a large microarray collection for leukemia biomarker identification. in F Fdez-Riverola, JF De Paz, MP Rocha, FJD Mayo & MS Mohamad (eds), 10th International Conference on Practical Applications of Computational Biology and Bioinformatics. Advances in Intelligent Systems and Computing, vol. 477, Springer Verlag, pp. 71-79, International Conference on Practical Applications of Computational Biology and Bioinformatics PACBB, 2016, Sevilla, Spain, 1/06/16. https://doi.org/10.1007/978-3-319-40126-3_8

Deep data analysis of a large microarray collection for leukemia biomarker identification. / Labaj, Wojciech ; Papiez, Anna ; Polanska, Joanna et al.
10th International Conference on Practical Applications of Computational Biology and Bioinformatics. ed. / Florentino Fdez-Riverola; Juan F. De Paz; Miguel P. Rocha; Francisco J. Domínguez Mayo; Mohd Saberi Mohamad. Springer Verlag, 2016. p. 71-79 (Advances in Intelligent Systems and Computing; Vol. 477).

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

TY - GEN

T1 - Deep data analysis of a large microarray collection for leukemia biomarker identification

AU - Labaj, Wojciech

AU - Papiez, Anna

AU - Polanska, Joanna

AU - Polanski, Andrzej

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2016.

PY - 2016

Y1 - 2016

N2 - Nowadays, statistical design of experiments allows for planning of complex studies while maintaining control over technical bias. In this study, the equal importance of performing tailored preprocessing, such as batch effect adjustment and adaptive signal filtration, is demonstrated in order to enhance quality of the results. This approach is assessed on a large set of data on acute and chronic leukemia cases. It is shown, both through statistical analysis and literature research, that drawing attention toward data preprocessing is worthwhile, as it produces meaningful original biological conclusions. Specifically in this case, it entailed the revealing of four candidate leukemia biomarkers for further investigation of their significance.

AB - Nowadays, statistical design of experiments allows for planning of complex studies while maintaining control over technical bias. In this study, the equal importance of performing tailored preprocessing, such as batch effect adjustment and adaptive signal filtration, is demonstrated in order to enhance quality of the results. This approach is assessed on a large set of data on acute and chronic leukemia cases. It is shown, both through statistical analysis and literature research, that drawing attention toward data preprocessing is worthwhile, as it produces meaningful original biological conclusions. Specifically in this case, it entailed the revealing of four candidate leukemia biomarkers for further investigation of their significance.

KW - Batch effect

KW - Biomarker identification

KW - Gene expression

KW - High-throughput

KW - Leukemia

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DO - 10.1007/978-3-319-40126-3_8

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SP - 71

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BT - 10th International Conference on Practical Applications of Computational Biology and Bioinformatics

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A2 - De Paz, Juan F.

A2 - Rocha, Miguel P.

A2 - Mayo, Francisco J. Domínguez

A2 - Mohamad, Mohd Saberi

PB - Springer Verlag

T2 - International Conference on Practical Applications of Computational Biology and Bioinformatics PACBB, 2016

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ER -

Labaj W , Papiez A , Polanska J , Polanski A. Deep data analysis of a large microarray collection for leukemia biomarker identification. In Fdez-Riverola F, De Paz JF, Rocha MP, Mayo FJD, Mohamad MS, editors, 10th International Conference on Practical Applications of Computational Biology and Bioinformatics. Springer Verlag. 2016. p. 71-79. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-319-40126-3_8

Deep data analysis of a large microarray collection for leukemia biomarker identification

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this