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

Wyniki badań: Rozdział w książce/raport/materiał konferencyjny › Wkład w konferencję › recenzja

Abstrakt

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.

Język oryginału	angielski
Tytuł publikacji goszczącej	10th International Conference on Practical Applications of Computational Biology and Bioinformatics
Redaktorzy	Florentino Fdez-Riverola, Juan F. De Paz, Miguel P. Rocha, Francisco J. Domínguez Mayo, Mohd Saberi Mohamad
Wydawca	Springer Verlag
Strony	71-79
Liczba stron	9
ISBN (drukowany)	9783319401256
Identyfikatory DOI	https://doi.org/10.1007/978-3-319-40126-3_8
Status publikacji	Opublikowano - 2016
Wydarzenie	International Conference on Practical Applications of Computational Biology and Bioinformatics PACBB, 2016 - Sevilla, Hiszpania Czas trwania: 1 cze 2016 → 3 cze 2016

Seria publikacji

Nazwa	Advances in Intelligent Systems and Computing
Tom	477
ISSN (drukowany)	2194-5357

Konferencja

Konferencja	International Conference on Practical Applications of Computational Biology and Bioinformatics PACBB, 2016
Kraj/Terytorium	Hiszpania
Miejscowość	Sevilla
Okres	1/06/16 → 3/06/16

Cele SDG ONZ

Ten wynik przyczynia się do realizacji następujących celów zrównoważonego rozwoju

Cel 3 Dobre zdrowie i dobre samopoczucie

Obszary tematyczne ASJC Scopus

Inżynieria sterowania i systemów
Informatyka ogólna

Uzyskaj dostęp do dokumentu

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

Inne pliki i linki

Link do publikacji w Scopus

Cytowanie

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

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

@inproceedings{a5ca9992e1144ab88bb89dbe5b2bfb0c,

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",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; International Conference on Practical Applications of Computational Biology and Bioinformatics PACBB, 2016 ; Conference date: 01-06-2016 Through 03-06-2016",

year = "2016",

doi = "10.1007/978-3-319-40126-3\_8",

language = "English",

isbn = "9783319401256",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Verlag",

pages = "71--79",

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",

address = "Germany",

}

Labaj, W , Papiez, A , Polanska, J & Polanski, A 2016, Deep data analysis of a large microarray collection for leukemia biomarker identification. w F Fdez-Riverola, JF De Paz, MP Rocha, FJD Mayo & MS Mohamad (red), 10th International Conference on Practical Applications of Computational Biology and Bioinformatics. Advances in Intelligent Systems and Computing, tom 477, Springer Verlag, str. 71-79, International Conference on Practical Applications of Computational Biology and Bioinformatics PACBB, 2016, Sevilla, Hiszpania, 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 i in.
10th International Conference on Practical Applications of Computational Biology and Bioinformatics. red. / Florentino Fdez-Riverola; Juan F. De Paz; Miguel P. Rocha; Francisco J. Domínguez Mayo; Mohd Saberi Mohamad. Springer Verlag, 2016. str. 71-79 (Advances in Intelligent Systems and Computing; Tom 477).

Wyniki badań: Rozdział w książce/raport/materiał konferencyjny › Wkład w konferencję › recenzja

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

UR - https://www.scopus.com/pages/publications/84976420398

U2 - 10.1007/978-3-319-40126-3_8

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

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AN - SCOPUS:84976420398

SN - 9783319401256

T3 - Advances in Intelligent Systems and Computing

SP - 71

EP - 79

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

A2 - Fdez-Riverola, Florentino

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

Y2 - 1 June 2016 through 3 June 2016

ER -

Labaj W , Papiez A , Polanska J , Polanski A. Deep data analysis of a large microarray collection for leukemia biomarker identification. W Fdez-Riverola F, De Paz JF, Rocha MP, Mayo FJD, Mohamad MS, redaktorzy, 10th International Conference on Practical Applications of Computational Biology and Bioinformatics. Springer Verlag. 2016. str. 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

Abstrakt

Seria publikacji

Konferencja

Cele SDG ONZ

Obszary tematyczne ASJC Scopus

Uzyskaj dostęp do dokumentu

Inne pliki i linki

Fingerprint

Cytowanie