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Using this structural network as a template, we inferred the topology of the human T-cell activation network by using the experimental dataset performing a Bayesian network approach (see methods).
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In the other hand, since Bayesian algorithms might lead to several different results using the same experimental dataset, we also feed the algorithm with a structural network template to decrease the number of different outputs and to increase its accuracy.
Nine input and three output parameters are used to train and test various neural network architectures in both multiple output and single output prediction paradigms using the available experimental datasets.
In Section 3, we describe the experimental dataset used for validating our work.
In particular, as the experimental dataset used to perform these tests is hypothesized to have a relatively high false-negative rate itself (Fossum et al., 2009), it is likely thigh false-negativeolutionaratevitself to improve the prediction oFossums is simply missing.
Functional analysis of DE genes was performed using Ingenuity Pathways Analysis (Ingenuity Systems), which analyzes the experimental dataset in the context of known biological response and regulatory networks in the Ingenuity Pathways Knowledge Base (IPKB).
The experimental dataset was used to validate a numerical one-dimensional CFD model of the sliding vane pump developed in the GT-SUITE™ environment.
The validity and consistency of the experimental dataset are evaluated using a simulation software package, modular modeling system (MMS).
The same approximation used for processing the experimental datasets applies to simulation data, then the same assumptions are considered.
The model constructed on the experimental dataset can be used to assign unknown samples to a previously defined class based on its measured features such as spectrum.
The following subsections present the details about the dataset used, the experimental setup, and the results of different types of experiments such as expression-based, illumination-based, gender-based, and age-based experiments.
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