The 2,026 human persistent strains and 1,018 avian strains were grouped by time, location and subtype, with representative samples chosen at random to yield 281 distinct human strains and 560 distinct avian strains. Classifier AZD3965 in vivo accuracy was estimated by randomly dividing BVD-523 supplier the data set into 5 non-overlapping partitions. The classifier was trained on 4 of the partitions and accuracy was measured by the percentage of correct classifications on the fifth partition, with the percentage of correct classifications calculated separately for each

class to account for the difference in class size. The average of all 5 tested non-overlapping partitions was calculated giving two accuracy values (one for each class) and the final accuracy measure was the average of these two values. The 34 pandemic conserved markers given in this report were required to be positively identified in every sequenced strain in each of the three pandemic outbreaks without deviation from the majority consensus. This led to three markers reported in [11] that were excluded from this report for lack of conservation or positive identification (when an ambiguous sequence code was present) in one of the sequenced strains associated with the pandemic outbreaks. The host specificity classifier misclassified 2 human and 2 avian strains for a classification accuracy of 99.5%. The

classification errors appeared to be due to recent reassortment events that suggest the presence of influenza genomes that are a mix of both human and avian strains [29]. The high 3-deazaneplanocin A mortality rate data set was constructed using the same procedure as the host type dataset and the same 5-fold cross validation procedure was used to estimate accuracy. A total of 111 influenza Ponatinib in vitro genomes were classified as high-mortality rate strains and 2,001 were classified as low-mortality rate strains, with a non-redundant subset taken for training (35 high mortality rate, and 255 low mortality rate). The percentage of high

and low mortality rate strains that were correctly classified was 96.2% and 96.9% respectively (an average of 96.6%). The lower accuracy for the high mortality rate classifier compared to the host type classifier likely highlights the genetic complexity associated with high mortality rate and the influence of other important factors such as host interaction. Newly generated classifiers using only a small subset of the aligned proteomes as input were required to match the original classifier accuracy (99.5% for host type and 96.6% for high mortality rate type) within a margin of error defined by a confidence threshold. The confidence thresholds were defined by confidence intervals assuming 1 sided t-test comparisons using the standard deviation in the cross validation tests.