Figure 5.

Model accuracy as a function of modeling method and data size. The figure shows the effects of using different sizes of data sets for fitting the models and the effects on the model accuracy as measured by the mean square error of prediction. The mean square error of predicted data using four different methods and using 10, 20, 40, 80, 160, and 320 data points, for both A549 and AG02603 cells are displayed. The four methods are two linear regression methods and two neural network methods. The linear regression methods include one that uses pairwise products of concentrations and quadratic terms of the concentrations (QRF), and the other uses the n-wise products of concentrations of drugs as interaction terms (LR). The two neural network methods are a cascaded neural network with two single-neuron layers, and a four-neuron single-layer multi-layer perceptron (MLP). As the number of points used to generate the model increases, the mean square error decreases.

Al-Shyoukh et al. BMC Systems Biology 2011 5:88   doi:10.1186/1752-0509-5-88
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