Figure 4.

Three-drug model fitting. The figure shows an evaluation of modeling the responses of A549 and AG02603 to combinations of three drugs. (A) The panels show a plot of the model predicted cellular ATP levels versus the experimentally measured values. Cellular ATP-level predictive models for A549 and AG02603 cells were developed using a number of different methods. a. A linear regression model that uses pairwise products of concentrations and quadratic terms (QRF). b. A linear regression model with n-wise products of concentrations (LR). c. A cascaded neural network with two single-neuron layers (Cascaded NNet). d. A four-neuron single layer multi-layer perceptron artificial neural network (MLP). The models are based on fitting 80 out of 512 combinations and the figure shows the predicted versus experimental values for all 512 combinations. The correlation between the experimentally tested cellular ATP-level (x-axis) and the predicted cellular ATP-level (y-axis) is shown. The circles in the graphs represent individual data points. The diagonal line represents a perfect fit between the experimental and predicted data. (B) Comparison between the predicted normalized ATP levels of different models. The predicted ATP levels for different models are plotted against each other. a. QRF versus LR. b. Cascaded NNet versus MLP. c. QRF versus MLP. d. LR versus MLP. The correlation coefficients between the different methods are shown.

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