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Statements

Subject Item
n2:RIV%2F00216305%3A26230%2F09%3APU86236%21RIV10-MSM-26230___
rdf:type
n14:Vysledek skos:Concept
dcterms:description
Microarrays as a promising technology for prediction of cancer diagnosis have attracted attention of many researchers in recent years. Researchers often neglect clinical data used for prediction of diagnosis compared to pre-microarray era. An important problem is determination of an additional predictive value of microarray data in relation to clinical variables. We propose a new two-step method combining logistic regression and BinomialBoosting models, to determine the additional predictive value of microarray data. This method is evaluated on two benchmark breast cancer datasets together with other already published method. The new method can combine clinical and microarray data more effectively and enables simple addition of various types of data into the combined prediction. <br> Microarrays as a promising technology for prediction of cancer diagnosis have attracted attention of many researchers in recent years. Researchers often neglect clinical data used for prediction of diagnosis compared to pre-microarray era. An important problem is determination of an additional predictive value of microarray data in relation to clinical variables. We propose a new two-step method combining logistic regression and BinomialBoosting models, to determine the additional predictive value of microarray data. This method is evaluated on two benchmark breast cancer datasets together with other already published method. The new method can combine clinical and microarray data more effectively and enables simple addition of various types of data into the combined prediction. <br>
dcterms:title
Additional Predictive Value of Microarray Data Compared to Clinical Variables Additional Predictive Value of Microarray Data Compared to Clinical Variables
skos:prefLabel
Additional Predictive Value of Microarray Data Compared to Clinical Variables Additional Predictive Value of Microarray Data Compared to Clinical Variables
skos:notation
RIV/00216305:26230/09:PU86236!RIV10-MSM-26230___
n3:aktivita
n13:P
n3:aktivity
P(2B06052)
n3:dodaniDat
n17:2010
n3:domaciTvurceVysledku
n15:7113838 n15:1574582
n3:druhVysledku
n6:D
n3:duvernostUdaju
n18:S
n3:entitaPredkladatele
n16:predkladatel
n3:idSjednocenehoVysledku
301956
n3:idVysledku
RIV/00216305:26230/09:PU86236
n3:jazykVysledku
n21:eng
n3:klicovaSlova
logistic regression, boosting, generalized linear models, prediction, microarray data, clinical data, breast cancer
n3:klicoveSlovo
n4:logistic%20regression n4:microarray%20data n4:clinical%20data n4:prediction n4:generalized%20linear%20models n4:boosting n4:breast%20cancer
n3:kontrolniKodProRIV
[84E74A47B869]
n3:mistoKonaniAkce
Sheffield
n3:mistoVydani
Sheffield
n3:nazevZdroje
PRIB 2009, 4th IAPR International Conference on Pattern Recognition in Bioinformatics
n3:obor
n20:JC
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n19:2B06052
n3:rokUplatneniVysledku
n17:2009
n3:tvurceVysledku
Šilhavá, Jana Smrž, Pavel
n3:typAkce
n12:EUR
n3:zahajeniAkce
2009-09-07+02:00
s:numberOfPages
6
n11:hasPublisher
University of Sheffield
n9:isbn
978-0-9563399-0-4
n8:organizacniJednotka
26230