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For the first time, our study proposes a criterion for validation of public datasets by providing 22 validation datasets from 31 available breast cancer datasets for survival analysis.
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Specifically, the pathways in Table 4 have the most significance across the five datasets for stratifying survival using the PRI.
To ensure that these results were not limited to the van de Vijver dataset, we obtained all additional publicly available breast cancer datasets for which survival and clinicopathological data were available for at least 150 samples (Table 1).
Long-term follow up data from this dataset was used for survival analysis.
These publicly available datasets meet common criteria for survival analysis [ 32].
None of the datasets (or samples) used for survival (or response prediction) were used to derive the SCMGENE or the PAM50 subtype predictor.
Our study provides a high-throughput validation method for assessing the prognostic value of all available public gene-expression signatures in breast cancer patients and 22 breast cancer datasets that are useful for survival analyses.
After filtering out individuals on the basis of exclusion criteria, the final dataset comprised 17,736 patients for survival analysis.
Also in the third one, the smaller Stockholm dataset, there was evidence for survival difference, but it did not reach statistical significance.. Altogether, the CHEK2 1100delC gene-expression signature was associated with more rapid relapse of the disease in all three sample sets.
We also selected two classes of survival datasets for classification.
Using this supervised analysis strategy, we found considerable concordance between the datasets for pathways involved in survival, proliferation, apoptosis, cell adhesion, extra cellular matrix signaling and remodeling, hypoxia and angiogenesis.
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CEO of Professional Science Editing for Scientists @ prosciediting.com