Národní úložiště šedé literatury Nalezeno 2 záznamů.  Hledání trvalo 0.00 vteřin. 
Molecular Signature as Optima of Multi-Objective Function with Applications to Prediction in Oncogenomics
Aligerová, Zuzana ; Maděránková, Denisa (oponent) ; Provazník, Ivo (vedoucí práce)
Content of this work is theoretical introduction and follow-up practical processing of topic Molecular signature as optima of multi-objective function with applications to prediction in oncogenomics. Opening chapters are targeted on topic of cancer, mainly on breast cancer and its subtype Triple Negative Breast Cancer. Succeeds the literature review of optimization methods, mainly on meta-heuristic methods for multi-objective optimization and problematic of machine learning. Part is focused on the oncogenomics and on the principal of microarray and also to statistics methods with emphasis on the calculation of p-value and Bimodality Index. Practical part of work consists from concrete research and conclusions lead to next steps of research. Implementation of selected methods was realised in Matlab and R, with use of other programming languages Java and Python.
Molecular Signature as Optima of Multi-Objective Function with Applications to Prediction in Oncogenomics
Aligerová, Zuzana ; Maděránková, Denisa (oponent) ; Provazník, Ivo (vedoucí práce)
Content of this work is theoretical introduction and follow-up practical processing of topic Molecular signature as optima of multi-objective function with applications to prediction in oncogenomics. Opening chapters are targeted on topic of cancer, mainly on breast cancer and its subtype Triple Negative Breast Cancer. Succeeds the literature review of optimization methods, mainly on meta-heuristic methods for multi-objective optimization and problematic of machine learning. Part is focused on the oncogenomics and on the principal of microarray and also to statistics methods with emphasis on the calculation of p-value and Bimodality Index. Practical part of work consists from concrete research and conclusions lead to next steps of research. Implementation of selected methods was realised in Matlab and R, with use of other programming languages Java and Python.

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