About: Network Constrained Forest to Improve Gene Expression Data Classification

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
rdfs:seeAlso	http://radio.feld.cvut.cz/conf/poster2014/
Description	Onset and progression of genetically determined diseases depend on complex process called gene expression. Integrating genomic measurements of multiple character from multiple stages of that process should improve diagnosis and and overall comprehension of the diseases. We propose a method, based on a concept of random forests, that utilizes traditional messenger RNA features and quite novel microRNA features. The methods integrates the features through domain knowledge in terms of interactions between microRNAs and their targeted messenger RNA, and interactions between proteins corresponding to the messenger RNA transcripts. Introducing prior knowledge should increase learning bias and consequently improve overall predictive accuracy, stability and comprehensibility of resulting model. We run several robust experiments to validate our method in comparison with state of the art methods. Our results suggest that out method in most of the cases achieves better or equal results. Henceforth, integration of genomic data with aid of prior knowledge has promising perspective. Onset and progression of genetically determined diseases depend on complex process called gene expression. Integrating genomic measurements of multiple character from multiple stages of that process should improve diagnosis and and overall comprehension of the diseases. We propose a method, based on a concept of random forests, that utilizes traditional messenger RNA features and quite novel microRNA features. The methods integrates the features through domain knowledge in terms of interactions between microRNAs and their targeted messenger RNA, and interactions between proteins corresponding to the messenger RNA transcripts. Introducing prior knowledge should increase learning bias and consequently improve overall predictive accuracy, stability and comprehensibility of resulting model. We run several robust experiments to validate our method in comparison with state of the art methods. Our results suggest that out method in most of the cases achieves better or equal results. Henceforth, integration of genomic data with aid of prior knowledge has promising perspective. (en)
Title	Network Constrained Forest to Improve Gene Expression Data Classification Network Constrained Forest to Improve Gene Expression Data Classification (en)
skos:prefLabel	Network Constrained Forest to Improve Gene Expression Data Classification Network Constrained Forest to Improve Gene Expression Data Classification (en)
skos:notation	RIV/68407700:21230/14:00219787!RIV15-MSM-21230___
http://linked.open...avai/riv/aktivita	S
http://linked.open...avai/riv/aktivity	S
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Anděl, Michael
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	České vysoké učení technické v Praze / Fakulta elektrotechnická
http://linked.open...dnocenehoVysledku	32212
http://linked.open...ai/riv/idVysledku	RIV/68407700:21230/14:00219787
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Gene expression; microRNA; Machine learning; Ran dom Forest; background knowledge (en)
http://linked.open.../riv/klicoveSlovo	Machine learning background knowledge Gene expression microRNA Ran dom Forest
http://linked.open...ontrolniKodProRIV	[B2780F809A6F]
http://linked.open...v/mistoKonaniAkce	Praha
http://linked.open...i/riv/mistoVydani	Prague
http://linked.open...i/riv/nazevZdroje	POSTER 2014 - 18th International Student Conference on Electrical Engineering
http://linked.open...in/vavai/riv/obor	IN
http://linked.open...ichTvurcuVysledku	1 (xsd:int)
http://linked.open...cetTvurcuVysledku	1 (xsd:int)
http://linked.open...UplatneniVysledku	2014
http://linked.open...iv/tvurceVysledku	Anděl, Michael
http://linked.open...vavai/riv/typAkce	EUR - Evropská
http://linked.open.../riv/zahajeniAkce	2014-05-15 (xsd:date)
number of pages	5 (xsd:int)
http://purl.org/ne...btex#hasPublisher	České vysoké učení technické v Praze
https://schema.org/isbn	978-80-01-05499-4
http://localhost/t...ganizacniJednotka	21230

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