One of the key questions is the sequence length that is required for accuracy with high probability, expressed as a function of the model tree parameters (number n of taxa and parameters f and g, defined to be the minimum and maximum edge lengths, respectively, where the length of an edge is the expected number of substitutions of a random site on that edge).
For classifiers trained on feature sets ranging from 1 to 1000 features, we plotted the maximum prediction accuracies of transcript- and marker-based prediction for each SMP; more or fewer features are required for maximum accuracy depending on the SMP.
The advantage of the newer methodology is that only very small sample amounts about 250 millilitres (one millilitre = 0.034 fluid ounce), are required for high accuracy measurements.
In summary, we show that PPRT is a practical high-throughput approach, although we would recommend multiplicate PPRTs per sample are required for sufficient accuracy for case-control analyses, and careful quality-control performed for every experiment.
In this case, at least ±10 flanking SNPs are required for the accuracy to be higher than 0.80.
However, additional validation tests are required for proving the accuracy of the proposed model.
To ensure a valid assessment of gene-gene and gene-environment interactions, standards are required for sample sizes, accuracy and precision for continuous data, specificity and sensitivity for discrete data and appropriate statistical methods.
The ability of the proposed method to select the most discriminatory features resulted in improved classification performance with a smaller number of features, although the number of features that are required for achieving high recognition accuracy varies from one database to another.
Therefore, although pluripotency scores of Oct3/4-target genes can be considered as the most important index for evaluation of porcine iPSCs, the DNA methylation profiles of all three groups making up the 36 genes are required for evaluation with high accuracy.
Furthermore, as compared to hp – adaptive PCFE, significantly less number of actual function evaluations are required for obtaining results of similar accuracy.
