Interaction-Based Aggregation of mRNA and miRNA Expression Profiles to Differentiate Myelodysplastic Syndrome

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00237846" target="_blank" >RIV/68407700:21230/15:00237846 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007%2F978-3-319-26129-4_11" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-319-26129-4_11</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-26129-4_11" target="_blank" >10.1007/978-3-319-26129-4_11</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Interaction-Based Aggregation of mRNA and miRNA Expression Profiles to Differentiate Myelodysplastic Syndrome

Popis výsledku v původním jazyce

In this work we integrate conventional mRNA expression profiles with miRNA expressions using the knowledge of their validated or predicted interactions in order to improve class prediction in genetically determined diseases. The raw mRNA and miRNA expression features become enriched or replaced by new aggregated features that model the mRNA-miRNA interaction. The proposed subtractive integration method is directly motivated by the inhibition/degradation models of gene expression regulation. The method aggregates mRNA and miRNA expressions by subtracting a proportion of miRNA expression values from their respective target mRNAs. Further, its modification based on singular value decomposition that enables different subtractive weights for different miRNAs is introduced. Both the methods are used to model the outcome or development of myelodysplastic syndrome, a blood cell production disease often progressing to leukemia. The reached results demonstrate that the integration improves classification performance when dealing with mRNA and miRNA features of comparable significance. The proposed methods are available as a part of the web tool miXGENE.

Název v anglickém jazyce

Interaction-Based Aggregation of mRNA and miRNA Expression Profiles to Differentiate Myelodysplastic Syndrome

Popis výsledku anglicky

In this work we integrate conventional mRNA expression profiles with miRNA expressions using the knowledge of their validated or predicted interactions in order to improve class prediction in genetically determined diseases. The raw mRNA and miRNA expression features become enriched or replaced by new aggregated features that model the mRNA-miRNA interaction. The proposed subtractive integration method is directly motivated by the inhibition/degradation models of gene expression regulation. The method aggregates mRNA and miRNA expressions by subtracting a proportion of miRNA expression values from their respective target mRNAs. Further, its modification based on singular value decomposition that enables different subtractive weights for different miRNAs is introduced. Both the methods are used to model the outcome or development of myelodysplastic syndrome, a blood cell production disease often progressing to leukemia. The reached results demonstrate that the integration improves classification performance when dealing with mRNA and miRNA features of comparable significance. The proposed methods are available as a part of the web tool miXGENE.

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Biomedical Engineering Systems and Technologies: Communications in Computer and Information Science

ISBN

978-3-319-26128-7

ISSN

1865-0929

e-ISSN

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Počet stran výsledku

16

Strana od-do

165-180

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Angers

Datum konání akce

3. 3. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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