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We specify a prognosis OSA by specifying (a) the function fulfilled by a generic prognosis module, and (b) the inputs and outputs that must be present in such a module.
The framework consists of three parts: (1) Identifying the prognosis module.
The prognosis module is selected based on the statistical differences between the good outcome and bad outcome of the patients in three breast cancer clinical trials.
(5) P = ∑ x ≥ n C N x ⋅ C M - N m - x C M m where M is the total number of genes in the whole profiles, N is the number of the prognosis gene modules, m is the number of the sensitivity module of the drug, and n is the number of the overlap between the prognosis module and the sensitivity module.
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It is based on supervision and prognosis modules which allow the estimation of the current state of the network, and the forecasting of the extreme event occurrence.
Combining with the prognosis modules and drug sensitivity modules, a repositioning framework for breast cancer is built.
As shown in the final step of Figure 1, a ranked list of the modules and the selected prognosis modules was generated with the most significant connection to the prognosis.
As shown in Table 2, the prognosis modules are most likely related to the biologic process of cell cycle, and microtubule related biologic processes and cellular components, which was generally reported that has close association with cancer progression [ 21].
The performance of the drug identification is assessed by the Effect Score which is the number of prognosis gene modules with significance divided by the half number of the identified prognosis gene modules.
We have identified 30 significant biological processes related to breast cancer prognosis gene modules.
In this study, we introduced a new pipeline to interrogate the transient cellular state under drug conditions by prognosis gene modules.
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