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It is a path-based fingerprint that identifies small molecule fragments based on all linear and ring substructures and maps them onto a bit-string using a hash function (somewhat similar to the daylight fingerprints [ 47, 48]).
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Since the overall spread of the peptides is highly similar across all the replicates based on a linear gradient, training the predictor on one of these runs is sufficient to learn this distribution.
To enable identification of segment status with new, independent samples, we created an identification tool based on a linear discriminant function of all 36 variables used in the segmentation analysis.
The forecasting included a prediction of future expenditure in all therapeutic areas based on a linear regression analysis applied to historical sales pattern, subsequently adjusted for factors likely to influence utilization and/or expenditure.
Based on a linear regression model, it drops about 3.5% for each typo noticed.
It is based on a linear scale ranging from 0 10.
Analysis was based on a linear model with correlated errors.
However, these conclusions were based on a linear PC analysis.
Statistical analyses were based on a linear mixed-effects model.
Taylor and Muller [ 3] and Muller and Pasour [ 4] derived exact distributions of noncentrality and power in univariate linear models based on all combinations of estimated variance and means.
To avoid this problem, and to measure the actual power of each variable, we used multimodal inference based on all-subsets selection of generalised linear mixed effects models (GLMM) ([53]).
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