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This enables us to improve prediction performance for NGS data, but we can also use the trained model to improve predictions based on Sanger sequencing data.
The objective of this study is to develop a novel approach that integrates prior biological knowledge in the form of the Ontology Fingerprint to infer cell-type-specific signaling networks via data-driven Bayesian network learning; and to further use the trained model to predict cellular responses.
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After the cross-validation of training set, the independent test set was evaluated by using the trained model with the highest accuracy.
By using the trained model of the platform, we are able to test against micro-expression recognition.
We used the trained model to identify labels of unseen data.
Next, using the trained model weights, we derive a temporal sensitivity measure to asses how the model utilized the sampled cortices and neurons in real-time during BMI testing.
a Q-value test for straight corridor, b Q-value test for circular corridor Fig. 6 Average score of 5 times evaluation by using the trained model of every 1000 iteration steps Fig. 7 The more complicated environment for further experiment.
Finally, the test sample is classified using the trained model, and the feature extraction method included the power spectrum, time-domain statistics, AR model, and the wavelet coefficients.
We trained the logistic regression model based on the these differentially expressed CE samples of Gm12878 and then used the trained model to predict the CE inclusion of K562, and vice versa.
By using the trained model and a suitable score cutoff we could reliably recognize the flanks for at least 63% of all sequences in each sample, see Figure 1.
We then used the trained model to infer states from test data and reported the resultant mark frequency in Table 2. Differences in mark frequencies between the training and test data were small, and the bias was 0.289,5 for the CRF model and 0.298,8 for the HMM.
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