Sentence examples for biased discovery from inspiring English sources

However, microarray- or pyrosequencing-based transcriptomic reads do not provide sufficient depth and coverage for the detection and quantitation of low expression genes, which may result in a biased discovery towards high expression genes.

The biased discovery of highly similar gene duplicates means that many of those recovered by aCGH are likely to be the products of evolutionarily recent events having occurred between closely related species.

Benjamini and Hochberg multiple testing correction was used to avoid a biased discovery rate [ 33] The Student t-test detected the mean expression ratio of replicates that differed significantly from one (i.e. an expression ratio of 1 means that there was no change in gene expression).

This pattern is consistent with a biased discovery process which exhibits properties of reinforcement, whereby commonly studied proteins are more likely to be further studied in the near future, and with triggering, whereby discoveries spur related discoveries in the PPI network neighborhood.

The statistical clarity of here reported results is, however, especially remarkable, considering that patient selection criteria in this study strongly biased against discovery of such statistical associations.

By targeting cDNAs from male antennae, we biased gene discovery towards genes potentially involved in male olfaction, including pheromone reception.

We biased gene discovery towards genes expressed in wing tissue during the developmental stages when patterns on the wings of butterflies are known to be determined (Table 1).

By targeting cDNAs from developing wings at the stages when pattern is specified, we biased gene discovery towards genes potentially involved in pattern formation.

By targeting cDNA from developing wings at the stages when pattern is specified, the EST project described here biased gene discovery towards genes potentially involved in pattern formation.

We cover all major steps of a typical study, including examining the data for potential biases, discovery of biomarkers, testing the robustness of the identified biomarkers, building predictive models and validating the results in independent data.

For example, some methods must be trained from external datasets such as from the COSMIC database, which introduces possible circularity to the analysis and biases the discovery of genes whose mutational impact has already been characterized.