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We applied our method to a previously published acute lymphoblastic leukaemia (ALL) dataset including four different groups [20].
These sequences define the ALL dataset.
The second more populated disease class is 'Nervous System Diseases' (19% diseases, ALL dataset), while approximately half of disease genes (51%, ALL dataset) are annotated to this class.
The ALL dataset consists of microarrays from 128 different individuals with acute lymphoblastic leukemia (ALL).
In Figure 2A the average performance of all methods on the ALL dataset is displayed.
For the ALL dataset, no method of analysis recovered the MLSA tree topology.
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The stemness-on modules contained 103 homolog modules (78 all-for-all and 25 one-for-all) and 59 functional modules (46 all-for-all and 13 one-for-all) (Dataset S1).
OG first proposed the idea of using the AML-ALL dataset to realize our algorithm.
As shown in Figure 2, the module gene sets had a high proportion of significant genes in all three scenarios, with the VEGAS-all dataset showing the largest difference.
As the B-ALL dataset described here was processed in a single batch and each sample analyzed relative to other members of the batch, the RMA procedure was utilized.
As previously observed for HF-IHU in Fig. 11, kNN also shows that the no-retweet line plots are almost overlapped with the All-Dataset line plot, yet slightly less overlap than the HF-IHU.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com