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Over 1000 microsatellite markers, developed for parentage and population genetics analyses in songbirds [ 68, 69], have been used to construct a predicted passerine genome map based on sequence similarity to the chicken genome.
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We have constructed a predicted rice MADS network consisting of 19 rice MADS-box family members which was determined partially by experiments in rice (Figure 2) which has been expanded using interologs from Arabidopsis, (de Folter et al. 2005) indicated by blue lines in Figure 2.
Finally, we constructed a predicted three-dimensional structural model of the MUTYH protein, using a homology modeling method to map variants in the predicted structure (Fig. 4A and B).
In our research, we use the Random Forest (RF) algorithm to construct a prediction model to predict whether an amino acid is in disordered region or not.
The CaMR construct contains a predicted CaM binding site in the L-NTD (aa 556 610) fused to the R domain (aa 611 1816).
As stated previously, an advantage of the MOGA is that it allows the user to "choose" whether to pursue constructing a strain predicted to have slightly lower growth rate but higher product yield versus a strain predicted to have a slightly higher growth rate but lower product yield (as can be seen in the scatter plots in Figure 6).
To obtain an evaluation dataset, we constructed a PCUE (Predicted genes, FL- cDNA, UniGene, and EST) database based on available genomic resources (see Materials and Methods).
Once the observed quality scores have been calculated, they are used to construct a mapping from predicted to observed values.
The aim of genetic prediction is to construct a model to predict the phenotype of interest using the genotype data from individuals for whom the (potentially future) phenotypic state is unknown.
In order to specifically screen for proteins that associate with the DNA binding domain of POT1, we constructed an allele predicted to contain the full OB fold necessary for DNA binding, but with a truncated TPP1 interacting domain.
Bayes theorem uses the training data as evidence (E) for a seen outcome (O), to construct a probability for predicting the outcome when the evidence is seen in the future [43].
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