Kimmel, Marek - red. ; Lachowicz, Mirosław - red. ; Świerniak, Andrzej - red.
Cancer Growth and Progression, Mathematical Problems and Computer Simulations
Recently, data on multiple gene expression at sequential time points were analyzed using the Singular Value Decomposition (SVD) as a means to capture dominant trends, called characteristic modes, followed by the fitting of a linear discrete-time dynamical system in which the expression values at a given time point are linear combinations of the values at a previous time point. ; We attempt to address several aspects of the method. To obtain the model, we formulate a nonlinear optimization problem and present how to solve it numerically using the standardMATLAB procedures. We use freely available data to test the approach. ; We discuss the possible consequences of data regularization, called sometimes "polishing", on the outcome of the analysis, especially when the model is to be used for prediction purposes. Then, we investigate the sensitivity of the method to missing measurements and its abilities to reconstruct the missing data. ; Summarizing, we point out that approximation of multiple gene expression data preceded by SVD provides some insight into the dynamics, but may also lead to unexpected difficulties, like overfitting problems.
Zielona Góra: Uniwersytet Zielonogórski
AMCS, volume 13, number 3 (2003) ; click here to follow the link
Biblioteka Uniwersytetu Zielonogórskiego
Oct 18, 2021
Oct 18, 2021
|Properties of a singular value decomposition based dynamical model of gene expression data||Oct 18, 2021|
Moldovan, Andreea Bot, Radu Ioan Wanka, Gert Beliczyński, Bartłomiej - red.
Aswani Kumar, Cherukuri Srinivas, Suripeddi Korbicz, Józef - red. Uciński, Dariusz - red.