Network-Constrained Forest for Regularized Omics Data Classification
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00221458" target="_blank" >RIV/68407700:21230/14:00221458 - isvavai.cz</a>
Výsledek na webu
<a href="http://dx.doi.org/10.1109/BIBM.2014.6999193" target="_blank" >http://dx.doi.org/10.1109/BIBM.2014.6999193</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/BIBM.2014.6999193" target="_blank" >10.1109/BIBM.2014.6999193</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Network-Constrained Forest for Regularized Omics Data Classification
Popis výsledku v původním jazyce
Contemporary molecular biology deals with a wide and heterogeneous set of measurements to model and understand underlying biological processes including complex diseases. Machine learning provides a frequent approach to build such models. However, the models built solely from measured data often suffer from overfitting, as the sample size is typically much smaller than the number of measured features. In this paper, we propose a random forest-based classifier that minimizes this overfitting with the aidof prior knowledge in the form of a feature interaction network. We illustrate the proposed method in the task of disease classification based on measured mRNA and miRNA profiles complemented by the interaction network composed of the miRNA-mRNA targetrelations and mRNA-mRNA interactions corresponding to the interactions between their encoded proteins. We demonstrate that the proposed network-constrained forest employs prior knowledge to increase learning bias and consequently to impro
Název v anglickém jazyce
Network-Constrained Forest for Regularized Omics Data Classification
Popis výsledku anglicky
Contemporary molecular biology deals with a wide and heterogeneous set of measurements to model and understand underlying biological processes including complex diseases. Machine learning provides a frequent approach to build such models. However, the models built solely from measured data often suffer from overfitting, as the sample size is typically much smaller than the number of measured features. In this paper, we propose a random forest-based classifier that minimizes this overfitting with the aidof prior knowledge in the form of a feature interaction network. We illustrate the proposed method in the task of disease classification based on measured mRNA and miRNA profiles complemented by the interaction network composed of the miRNA-mRNA targetrelations and mRNA-mRNA interactions corresponding to the interactions between their encoded proteins. We demonstrate that the proposed network-constrained forest employs prior knowledge to increase learning bias and consequently to impro
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
IN - Informatika
OECD FORD obor
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Návaznosti výsledku
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)
Ostatní
Rok uplatnění
2014
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
Proceedings 2014 IEEE International Conference on Bioinformatics and Biomedicine
ISBN
978-1-4799-5668-5
ISSN
—
e-ISSN
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Počet stran výsledku
8
Strana od-do
410-417
Název nakladatele
IEEE
Místo vydání
Piscataway
Místo konání akce
Belfast
Datum konání akce
2. 11. 2014
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
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