Sentence examples for identification of biases from inspiring English sources

A more widely sampled set of training data incorporating increased diversity in projectile and target materials would likely improve the network internal weighting for material properties and avoid accidental identification of biases in the training exemplars.

In contrast, the methodology that we present focuses on identification of biases in the short-read data itself.

These studies aim to provide data on the effectiveness of interventions and to improve methods for the identification of biases that undermine the value of effectiveness data.

Identification of biases is possible only in an evidence synthesis framework, through the "triangulation" of multiple data sources: each source on its own provides a (potentially biased) view of only one aspect of the severity of an epidemic.

Detailed quality control of samples was carried out with R software: Normalized Unscaled Standard Error (NUSE) and Relative Log Expression (RLE) for global quality of signals in each array assessment, and MA plots before and after RMA for identification of biases associated with specific intensity classes.

Identification of bias in short-read data has been explored in other work, such as that of Dohm et al. [10] and Harismendy et al. [11].

Explicit and transparent references to supporting evidence will assist in the identification of bias rooted in special interests in the recommendations of governmental reports.

This statistical testing framework allows for identification of biased splicing only when there are enough informative ESTs to rule out the possibility that each choice is equally likely.

Furthermore, the identification of possible biases is a crucial point: Normative interests can lead to bias in the interpretation of the empirical data, and the state of empirical research may lead to a bias in the formulation of the normative question.

Two new approaches are presented for improved identification of measurement biases in linear pseudo steady-state processes.

Furthermore, identification of potential biases in existing datasets is complicated by the fact that usually not more than one independently generated dataset exists, making it very difficult to infer any biases post hoc.