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In this test, merging data sets improved overall survival prediction and risk association.
In this work, we analyzed the potential benefits of merging data sets for prognostic application in breast cancer diagnosis.
The survival prediction accuracy and prognosis of clinical risk were neither increased nor decreased significantly by merging data sets.
A partial improvement of the association of the 100-gene signatures with OS (measured by HR) by merging data sets can also be observed in Table S1 and Table S2.
Moreover, merging data sets from different studies helps to better understand the biases of individual studies and can lead to the identification of strong survival factors like CYB5D1 expression.
As Table 5 and Table 6 show, merging data sets improves the association of the gene signatures with OS compared to only one data set (out of three) and increases the HR of the gene signatures with respect to RFS for four out of five data sets.
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The crystals of the bosentan-bound ETB-Y4-mT4L were quite tiny (Supplementary Fig. 1e), but an automated data-collection and processing system (K.Y., G. Ueno, K. Hasegawa, M. Yamamoto and K.H., unpublished data) allowed us to collect and merge data sets from numerous crystals.
Leaders, however, should ensure there is a clear plan and infrastructure in place to collect and merge data sets across the institution.
"This means making it easy for the people to upload, to merge data sets, to discuss the data, to create visualizations and then to take these visualizations and put them elsewhere on the Web".
We use survival prediction based on Cox regression as an assay to measure the added value of merged data sets.
Taken all these together, however, the improvement seen with selectively merged data sets is relatively modest as compared to the prognostic power generated from the merged data sets composed of all OS single data sets.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com