Current Search: Martinenko, Evgeny (x)
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Title
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Functional Data Analysis and its application to cancer data.
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Creator
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Martinenko, Evgeny, Pensky, Marianna, Tamasan, Alexandru, Swanson, Jason, Richardson, Gary, University of Central Florida
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Abstract / Description
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The objective of the current work is to develop novel procedures for the analysis of functional dataand apply them for investigation of gender disparity in survival of lung cancer patients. In particular,we use the time-dependent Cox proportional hazards model where the clinical information isincorporated via time-independent covariates, and the current age is modeled using its expansionover wavelet basis functions. We developed computer algorithms and applied them to the dataset which is...
Show moreThe objective of the current work is to develop novel procedures for the analysis of functional dataand apply them for investigation of gender disparity in survival of lung cancer patients. In particular,we use the time-dependent Cox proportional hazards model where the clinical information isincorporated via time-independent covariates, and the current age is modeled using its expansionover wavelet basis functions. We developed computer algorithms and applied them to the dataset which is derived from Florida Cancer Data depository data set (all personal information whichallows to identify patients was eliminated). We also studied the problem of estimation of a continuousmatrix-variate function of low rank. We have constructed an estimator of such functionusing its basis expansion and subsequent solution of an optimization problem with the Schattennormpenalty. We derive an oracle inequality for the constructed estimator, study its properties viasimulations and apply the procedure to analysis of Dynamic Contrast medical imaging data.
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Date Issued
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2014
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Identifier
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CFE0005377, ucf:50447
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Format
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Document (PDF)
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PURL
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http://purl.flvc.org/ucf/fd/CFE0005377
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Title
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Prediction of survival of early stages lung cancer patients based on ER beta cellular expressions and epidemiological data.
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Creator
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Martinenko, Evgeny, Shivamoggi, Bhimsen, Chow, Lee, Peale, Robert, Brandenburg, John, University of Central Florida
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Abstract / Description
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We attempted a mathematical model for expected prognosis of lung cancer patients based ona multivariate analysis of the values of ER-interacting proteins (ERbeta) and a membranebound, glycosylated phosphoprotein MUC1), and patients clinical data recorded at the timeof initial surgery. We demonstrate that, even with the limited sample size available to use,combination of clinical and biochemical data (in particular, associated with ERbeta andMUC1) allows to predict survival of lung cancer...
Show moreWe attempted a mathematical model for expected prognosis of lung cancer patients based ona multivariate analysis of the values of ER-interacting proteins (ERbeta) and a membranebound, glycosylated phosphoprotein MUC1), and patients clinical data recorded at the timeof initial surgery. We demonstrate that, even with the limited sample size available to use,combination of clinical and biochemical data (in particular, associated with ERbeta andMUC1) allows to predict survival of lung cancer patients with about 80% accuracy whileprediction on the basis of clinical data only gives about 70% accuracy. The present work canbe viewed as a pilot study on the subject: since results conrm that ER-interacting proteinsindeed inuence lung cancer patients' survival, more data is currently being collected.
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Date Issued
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2011
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Identifier
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CFE0004134, ucf:49120
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Format
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Document (PDF)
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PURL
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http://purl.flvc.org/ucf/fd/CFE0004134
