miXGENE tool for learning from heterogeneous gene expression data using prior knowledge

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00218532" target="_blank" >RIV/68407700:21230/14:00218532 - isvavai.cz</a>

Výsledek na webu

<a href="http://mixgene.felk.cvut.cz/" target="_blank" >http://mixgene.felk.cvut.cz/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CBMS.2014.8" target="_blank" >10.1109/CBMS.2014.8</a>

Jazyk výsledku

angličtina

Název v původním jazyce

miXGENE tool for learning from heterogeneous gene expression data using prior knowledge

Popis výsledku v původním jazyce

High-throughput genomic technologies have proved to be useful in the search for both genetic disease markers and more complex predictive and descriptive models. By the same token, it became obvious that accurate and interpretable models need to concern more than raw measurements taken at a single phase of gene expression. In order to reach a deeper understanding of the molecular nature of complexly orchestrated biological processes, all the available measurements and existing genomic knowledge need to be fused. In this paper, we introduce a tool for machine learning from heterogeneous gene expression data using prior knowledge. The tool is called miXGENE, it is elaborated upon in close connection with the biological departments that dispose of the above-mentioned data and have a strong interest in their integration within particular problem-oriented projects. The main idea is not merely to capture the transcriptional phase of gene expression quantified by the amount of messenger RNA~(m

Název v anglickém jazyce

miXGENE tool for learning from heterogeneous gene expression data using prior knowledge

Popis výsledku anglicky

High-throughput genomic technologies have proved to be useful in the search for both genetic disease markers and more complex predictive and descriptive models. By the same token, it became obvious that accurate and interpretable models need to concern more than raw measurements taken at a single phase of gene expression. In order to reach a deeper understanding of the molecular nature of complexly orchestrated biological processes, all the available measurements and existing genomic knowledge need to be fused. In this paper, we introduce a tool for machine learning from heterogeneous gene expression data using prior knowledge. The tool is called miXGENE, it is elaborated upon in close connection with the biological departments that dispose of the above-mentioned data and have a strong interest in their integration within particular problem-oriented projects. The main idea is not merely to capture the transcriptional phase of gene expression quantified by the amount of messenger RNA~(m

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

—

Projekt

<a href="/cs/project/NT14539" target="_blank" >NT14539: XGENE.ORG -- veřejný nástroj integrované analýzy transkripčních, miRNA and metylačních dat</a><br>

Návaznosti

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

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

2014 IEEE 27th International Symposium on Computer-Based Medical Systems

ISBN

978-1-4799-4435-4

ISSN

1063-7125

e-ISSN

—

Počet stran výsledku

4

Strana od-do

247-250

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

New York

Datum konání akce

27. 5. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000345222200049