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The workflow for generating REGS is exemplified for the haematological malignancy DLBCL.
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The workflows for generating these models are shown in Figs. 17 and 18, and summary of the models is presented in Fig. 1.
The aim of the article was to establish a workflow for generating REGSs capable of predicting the clinical outcome of cancer patients.
Our workflow for generating the perturbation graph consists of three sequential modules: (i) planning and conducting perturbation experiments; (ii) generation of signed perturbation graph from experimental data; and (iii) assign edge weights (reflecting association strengths) from correlation measures.
In this report, we outline a systems biology workflow for generating and integrating data focusing on the TF regulator.
We aimed to develop an inexpensive, clinically feasible workflow for generating patient-specific, soft, custom silicone boluses (SCSBs) for head-and-neck (HN) radiation therapy.
While this may change the workflow for power users, its value to mainstream users is questionable.
The workflow for the analysis is shown in Fig. 2.
Figure 1 illustrates the workflow for the proposed strategy.
Figure 1 provides the workflow for the MMDS system.
The workflow for the analysis is described in Figure 1.
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