Thus, not only can K a / K s detect drug resistance mutations in a single dataset (with no requirement for a reference dataset of untreated samples), it can do so even when the primary dataset is contaminated with a large fraction of untreated samples, or samples with different treatments.
However, the important observation is that deep learning performs better than the Random Forest models even when the training dataset size is in ~103 104.
Even when the US1 dataset is examined, 9,347 (61.3%) and 610 (4.0%) genes showed non-synonymous and nonsense changes, respectively.
Even when the individual dataset is relatively large compared to the literature, the proposed method will still result in an estimate closer to the underlying multivariable association than the standard method ignoring the literature.
Our analysis of the HGDP dataset shows that our method is able to cluster populations even when the individuals in the dataset are admixed.
Even when the entities between datasets are mapped to each other, it can still be difficult to intuitively write queries that span datasets because of this heterogeneity.
Even when the limitations of this dataset are taken into account, those prediction models that have adequate generalizability should perform similarly in this dataset, as if it were a mixed dataset of both ventilated and non-ventilated infants.
The tree is highly sensitive to changes in input structures and the calculated features, making it very difficult to compare results across datasets even when the same clustering and fingerprinting algorithms are used.
Furthermore, the stochastic coalescent and mutational processes being modeled predict a large amount of variation in possible datasets even when the parameter values are known.
For the Plasmodium falciparum and HBV samples described here, the long march extended the amount of genome coverage within a dataset of a fixed number of reads, even when that dataset was relatively small.
