Deformation prediction by a feed forward artificial neural network during mouse embryo micromanipulation

In this study, a neural network (NN) modeling approach has been used to predict the mechanical and geometrical behaviors of mouse embryo cells. Two NN models have been implemented. In the first NN model dimple depth (w), dimple radius (a) and radius of the semi-circular curved surface of the cell (R...

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Bibliographic Details
Published in:Animal cells and systems Vol. 16; no. 2; pp. 121 - 126
Main Authors: Abbasi, Ali A., Sharif University of Technology, Tehran, Iran, Vossoughi, G.R., Sharif University of Technology, Tehran, Iran, Ahmadian, M.T., Sharif University of Technology, Tehran, Iran
Format: Journal Article
Language:English
Published: Daejeon Taylor & Francis Group 01.04.2012
Taylor & Francis Ltd
한국통합생물학회
Subjects:
artificial neural network
Behavior
biological cells
Deformation
Embryos
error back propagation algorithm
NEURAL NETWORKS
REDES DE NEURONAS
RESEAU DE NEURONES
Sensitivity analysis
Studies
생물학
ISSN:1976-8354, 2151-2485
Online Access:Get full text
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Summary:In this study, a neural network (NN) modeling approach has been used to predict the mechanical and geometrical behaviors of mouse embryo cells. Two NN models have been implemented. In the first NN model dimple depth (w), dimple radius (a) and radius of the semi-circular curved surface of the cell (R) were used as inputs of the model while indentation force (f) was considered as output. In the second NN model, indentation force (f), dimple radius (a) and radius of the semi-circular curved surface of the cell (R) were considered as inputs of the model and dimple depth was predicted as the output of the model. 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. Experimental data deduced by Fluckiger (2004) were collected to obtain training and test data for the NN. The results of these investigations show that the correlation values of the test and training data sets are between 0.9988 and 1.0000, and are in good agreement with the experimental observations.
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G704-000140.2012.16.2.005
ISSN:1976-8354
2151-2485
DOI:10.1080/19768354.2011.629680