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The ANN models trained and validated by representative data generally outperform those by using random data.

In the present work the purpose of using DOE is twofold: to define the optimal experiments which maximize the ratio of the model accuracy, and to minimize the number of necessary experiments (ANN models trained and validated).

In general 10-fold cross validation model values are lower for precision and accuracy (decreased R, increased MSE) than models trained and tested on the entire dataset due to the overfitting problem.

However, cell-to-cell variability is not commonly included in predictability analyses, thus relying on predictive models trained and tested on central tendency values.

Figure 5 shows ROC curves for the 3 models trained and tested on the set that was cleansed from proteins that are potentially subject to experimental bias.

For each of one thousand pairs of ANN and logistic models (trained and tested on the same datasets), these indices (area under the ROC curves, HL statistics and accuracy rate) were calculated and compared using paired T-tests (P < 0.05).

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After histogram normalization, it is possible to perform model training and test with leave-one-out CV.

A quality standard ensures that only AI models trained properly and working as expected would make it into the market.

The necessary computation time for both the models' training and evaluation in each test is measured and compared.

All the models were trained and tested using NVIDIA DIGITS DevBox.

The other creates output for machines to consume like models, training data, and algorithms.

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