The number of "bad" predictions drops from 65% to just 27%.
We show that the quality of their predictions drops when applied to more recent crystallization trails, which calls for new solutions.
Wind speed had a strong effect, e.g., predictions dropped by 80% as winds increased from 1 to 10 m s-1.
We have not been able to confirm whether SNP density does truly increase with graph complexity, and therefore, cannot rule out whether SNP prediction drop off in more complex regions is a true property of the graph or a failure of the 2 k + 2 algorithm to detect in these regions.
However, the sensitivity of prediction drops to as low as 50% when predicting transcription units with more than one gene [ 56].
The results of our LOOCV like test show that, our approach still achieved decent prediction accuracy, with AUPR 79.0 for direct interaction prediction, but the AUPR score for indirect interaction prediction dropped to 59.1.
If data on two synonymous SNPs (C282T and C481T) are removed and only non-synonymous SNPs are used, the accuracy of the prediction drops from 99.9 to 93.2%, with similar declines in SN and SP (Table 1).
The results in Table 5 show that, given the same number of records used for training, when marker effects from 10 chromosomes were included in the model, the accuracy of prediction dropped.
When genomes from the actual host species are excluded, the accuracy of host prediction drops slightly (95%, 83%/30%, 67%/30% for the same distance ranges), and even more when all genomes from the host genus are excluded (70% and 37% at the family level, no correct genus could be predicted in that case, and only one distance lower than 4.10−04 was observed, Table 1).
Modification of the calculated inviscid spreading radius using a linear viscous correction provides an improved prediction of drop spreading.
