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mouse-embryos
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Deformation prediction of mouse embryos in cell injection experiment by a feedforward artificial neural network
, Article Proceedings of the ASME Design Engineering Technical Conference, 28 August 2011 through 31 August 2011 ; Volume 2, Issue PARTS A AND B , August , 2011 , Pages 543-550 ; 9780791854792 (ISBN) ; Ahmadian, M. T ; Vossoughi, G. R ; Sharif University of Technology
2011
Abstract
In this study, neural network models have been used to predict the mechanical behaviors of mouse embryos. In addition, sensitivity analysis has been carried out to investigate the influence of the significance of input parameters on the mechanical behavior of mouse embryos. In order to reach these purposes two neural network models have been implemented. Experimental data earlier deduced-by [Flückiger, M. (2004). Cell Membrane Mechanical Modeling for Microrobotic Cell Manipulation. Diploma Thesis, ETHZ Swiss Federal Institute of Technology, Zurich, WS03/04]-were collected to obtain training and test data for the neural network. The results of these investigations show that the correlation...
Comparison of mouse embryo deformation modeling under needle injection using analytical Jacobian, nonlinear least square and artificial neural network techniques
, Article Scientia Iranica ; Volume 18, Issue 6 , 2011 , Pages 1486-1491 ; 10263098 (ISSN) ; Ahmadian, M. T ; Vossoughi, G. R ; Sharif University of Technology
Abstract
Analytical Jacobian, nonlinear least square and three layer artificial neural network models are employed to predict deformation of mouse embryos under needle injection, based on experimental data captured from literature. The Maximum Absolute Error (MAE), coefficient of determination ( R2), Relative Error of Prediction (REP), Root Mean Square Error of Prediction (RMSEP), NashSutcliffe coefficient of efficiency ( Ef) and accuracy factor ( Af) are used as the basis for comparison of these three models. Analytical Jacobian, nonlinear least square and ANN models have yielded the correlation coefficient of 0.9985, 0.9964 and 0.9998, respectively. The REP between the models predicted values and...