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While this result suggested that different prognostic gene signatures may track a common set of biological characteristics, the question remains that why there is a lack of consensus gene expression models for prognosis.
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Logistic regression modeling for prognosis in the ICU is still the current standard.
We further built a predictive model for prognosis based on the cell cycle gene signature and found our model to be more accurate than the Amsterdam 70-gene signature when tested with multiple gene expression datasets generated from several patient populations.
Results of logistic regression are summarized in Tables 3 and 4. Baseline values of six models used for prognosis assessment are provided in Table 2.
This work presents a methodology based on mesh morphing techniques for transfer of high-fidelity X-ray computed tomography (CT) data, including manufacturing defects information, into finite element (FE) models for failure prognosis in composite structures.
This framework will extend to incorporate any form of data, including emerging forms of genomic data, and provides a platform for development of models for personalized prognosis.
Cancer survival studies are commonly analyzed using survival-time prediction models for cancer prognosis.
Our most important finding is that incorporating higher-order representative features leads to improved prediction performance, which may help build better predictive models for cancer prognosis.
Measurements of functional CFTR protein in human models for diagnosis, prognosis, and personalized therapy have been initiated and used and are a step forward in the management of CF [ 27].
Accurate and objective models of prognosis for CAP will help physicians to assess a patient's risk profile and improve the decisions about hospitalization and treatment.
transparent reporting of a multivariable prediction model for individual prognosis or diagnosis.
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