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MammaPrint is a prognostic and predictive diagnostic test for early stage breast cancer patients that assess the risk that a tumor will metastasize to other parts of the body. [1] It gives a binary result, high-risk or low-risk classification, and helps physicians determine whether or not a patient will benefit from chemotherapy.
The basic technique of compact letter display is to label variables by one or more letters, so that variables are statistically indistinguishable if and only if they share at least one letter. The problem of doing so, using as few distinct letters as possible can be represented combinatorially as the problem of computing an edge clique cover of ...
UpSet plots are a data visualization method for showing set data with more than three intersecting sets. UpSet shows intersections in a matrix, with the rows of the matrix corresponding to the sets, and the columns to the intersections between these sets (or vice versa). The size of the sets and of the intersections are shown as bar charts.
The results aid physicians in deciding appropriate treatment for each patient. There are four breast cancer tests within the Symphony suite: MammaPrint , which assists physicians and patients in complex chemotherapy decisions by giving a clear and unambiguous high or low result of the risk of metastasis during the period when chemotherapy is ...
A box plot of the data set can be generated by first calculating five relevant values of this data set: minimum, maximum, median (Q 2), first quartile (Q 1), and third quartile (Q 3). The minimum is the smallest number of the data set. In this case, the minimum recorded day temperature is 57°F. The maximum is the largest number of the data set.
One set, the bootstrap sample, is the data chosen to be "in-the-bag" by sampling with replacement. The out-of-bag set is all data not chosen in the sampling process. When this process is repeated, such as when building a random forest, many bootstrap samples and OOB sets are created. The OOB sets can be aggregated into one dataset, but each ...
In cluster analysis, the elbow method is a heuristic used in determining the number of clusters in a data set. The method consists of plotting the explained variation as a function of the number of clusters and picking the elbow of the curve as the number of clusters to use.
Excel maintains 15 figures in its numbers, but they are not always accurate; mathematically, the bottom line should be the same as the top line, in 'fp-math' the step '1 + 1/9000' leads to a rounding up as the first bit of the 14 bit tail '10111000110010' of the mantissa falling off the table when adding 1 is a '1', this up-rounding is not undone when subtracting the 1 again, since there is no ...