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On a dataset of 96 cases with 192 images, they obtained a cluster-based sensitivity of 70, 80, and 90%% at 0.12, 0.61, and 1.49 FPs/image, respectively.
By normalizing the link weight between cancers based on the cancer's mutation rate, we obtained a clustering less influenced by the number of mutations occurring within each cancer.
These results imply that DMCNF can obtain a cluster structure with high cohesion.
Because of the normalisation procedure we used, we obtain a cluster where USA is linked with ex-USSR countries and show a similar decreasing behaviour in the latest decades (impossible to observed in a non-normalised context).
Therefore, in order to obtain a cluster center set with clearly distinct behaviors to seed the k-means algorithm in the next step, we manually grouped the cluster centers based on the similarity between them (Euclidian distance) and eliminate the centers of the less descriptive clusters from each group.
Template input was adjusted to obtain a cluster density of 700 900 K/mm2.
The cluster adjustment uses the delta-method with the between cluster variance estimate [ 36, 37] to obtain a cluster adjusted interval estimate for the Mantel Haenszel odds ratio.
One then regroups the two closest tumours and proceeds by regrouping tumours to obtain a cluster tree, which can be split into branches by selecting a cutoff distance.
To obtain a cluster as large as possible, we extracted relative GM volumes from nominal clusters in bilateral inferiror frontal gyri at the lenient uncorrected threshold of p < 0.001 and cluster sizes > 100.
In contrast to H AMMER, we do not distinguish whether the cluster is a singleton (i.e., |C| = 1); there may be plenty of superfluous clusters with several k-mers obtained by chance (actually, it is more likely to obtain a cluster of several k-mers by chance than a singleton of the same total multiplicity).
In WB tissue, the cluster with an elevated H3K4me2 marking downstream of TSS was further divided into four clusters using K-means (k = 4, repeats = 50, distance = square Euclidean) to obtain a cluster of 356 genes with highly elevated H3K4me2 labeling downstream of TSS.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com