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In summary, we compared prediction models that were developed with random intercept or standard logistic regression analysis in clustered data.
The model developed with random effect analysis showed better discrimination than the standard approach, if the cluster effects were used for risk prediction (standard c-index of 0.69 versus 0.66).
In general, the models developed with random forest and kappa nearest neighbor were reasonably good in predicting the test set (accuracy 67 70%), with random forest performing slightly better (MCC 0.41 vs 0.34 for kappa nearest neighbor; G-mean (0.66/0.70).
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Tumors that consist of stem cells and non-stem progenitors and develop with random motility alone, i.e. without environmental gradients, exhibit a self-metastatic phenotype.
For ξ = 1, the tumors after t = 360 days consist on average of 21,765 ± 1,505 cells (110 ± 9 cancer stem cells), a significant increase (p < 10-6) compared to tumors developing with random motility alone (7,023 ± 484 (43 ± 4) cells, Figure 2D).
Different representative volume elements (RVEs) are developed with a random distribution of fibres.
Figure 15 shows the progressive evacuation of oil from a reservoir developed with a random pattern of 14 producer wells; no injection wells occur.
Genetic algorithm (GA) is developed with Roulette wheel selection, arithmetic crossover, random mutation and applied to evaluate the minimum total cost and the corresponding optimum decision variables.
For the wheat 3B chromosome physical map developed with the SNaPshot technology, the average random overlap of bands was ~10% [ 23].
Models were developed with the cancer team as a random effect (at level 2), allowing for within-team correlation among patient outcomes.
However, SSR markers developed with conventional approaches are derived from the genome more or less in a random manner.
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