Network-Constrained Forest for Regularized Omics Data Classification

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>

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

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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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

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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