According to the parameters estimated from this training sample, we predicted all potential hybrids that between the 120 inbred lines in the training population and 3023 rice varieties in the 3 K RGP using the GBLUP method.
Using the three constructed models with 50 samples, we predicted zinc leaching efficiency of the remained 30 samples (Fig. 7).
From the four samples, we predicted 2753 functions using a consensus threshold of three (see Additional file 9).
After the model was fitted to the 136 counties for which we had AIV samples, we predicted the number of AIV cases in the other 2973 US counties by applying the model to the unsampled counties.
fFalse discovery rate, which was set to less than 5 % To investigate the diverse functions of proteins obtained from the three samples, we predicted the biological functions of the identified proteins using Gene Ontology (GO) analysis (http://www.geneontology.org).org
We derive an expression for the bandwidth of multicolor soliton generation in two-period QPM samples and we predict and confirm numerically that the bandwidth is broader in the two-period QPM sample than in homogeneous structures.
Thus, each segment represented an average class probability for each sample, and we predicted each sample to the class with the highest average probability.
Indeed, though we did not find significant GE interaction between the 10-SNP-set and environments such as Classroom Chaos and Harsh Parental Discipline in our sample, we would predict that significant GE interactions with measures such as these will emerge in the future.
If there are systematic differences in RNA quality between two classes of samples being compared, we predict that the effect of RNA quality on relative estimates of gene expression levels would be responsible for much of the signal in the data.
For this reason we did not continue to analyse all the available samples using the Ion AmpliSeq panel after our initial evaluation of 24 samples as we predicted a higher failure rate for the remaining sub-optimal samples.
By considering the change of pitch angle and predicted terrain angle is small between two samples, we can predict state 2 using state 1 information.
