Exact(2)
We therefore performed two additional analyses in order to determine the potential contribution of built-in dataset factors.
With these almost-zero p-values, we can conclude that built-in dataset factors are not responsible for the coordination rate achieved by MiNeGS and, thus, that MiNeGS is able to effectively identify functional NFPs.
Similar(58)
For each dataset, two factors were varied in the study: training set size (N) and machine learning method (M).
We discuss the overall statistics of a genome-wide functional dataset on factors involved in Hepatitis C Virus replication in human cells and describe one more detailed example with potential mechanistic insight.
*Measure as used in the analysis, and not necessarily the form in the original dataset Demographic factors consisted of age, gender, literacy in English language, education and economic status.
In this dataset, the factor names, including the operational taxonomic units and environmental factors, are previously defined by Steele et al. [ 2].
Using these thermal datasets, two-factor and multi-factor surrogate models are trained and several comparison simulations are conducted.
A high significant p-value (p <.001) indicates an appropriate dataset for factor analysis.
The uncorrelated lognormal clock model was determined to be a significantly better fit than the strict clock model for each of the datasets (logBayes factors ≫ 2).
A quantile - quantile plot analysis indicated, that this procedure did not sufficiently account for population stratification in our dataset (inflation factor λ = 1.34).
In both the cell line and human brain datasets, the factors treatment and array are partially confounded (since one cannot assign every treatment to each array).
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