Improved Disease Outcome Prediction Based on Microarray and Clinical Data Combination and Pre-validation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F10%3APU86237" target="_blank" >RIV/00216305:26230/10:PU86237 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Improved Disease Outcome Prediction Based on Microarray and Clinical Data Combination and Pre-validation
Original language description
Combining relevant information from high-dimensional microarray data and low-dimensional clinical variables to predict disease outcome is important to improve treatment decisions. Such a combination may yield more accurate predictions than those obtainedbased on the use of microarray or clinical data alone. We propose a combination of logistic regression for clinical data and BinomialBoosting for microarray data. Then we propose its extension designed for redundant sets of data. Our approach combines microarray and clinical data at the level of decision integration. The extension includes pre-validation of models built with microarray and clinical data followed by weights calculation. Weights determine relevance of microarray and clinical models for data combination. Evaluations are performed with several redundant and non-redundant simulated datasets. Then some tests are applied to two real benchmark datasets. Our approach increases outcome prediction on non-redundant simulated datas
Czech name
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Czech description
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Project
<a href="/en/project/2B06052" target="_blank" >2B06052: Determination of markers, screening and early diagnostics of cancer diseases using highly automated processing of multidimensional biomedical images</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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