Sentence examples for determining false from inspiring English sources

Moreover, it may pose practical difficulties in assessing HER2 expression in biopsies with high-grade dysplasia transiting to carcinoma, determining false positive results in biopsies, due to the misinterpretation of HER2-positive dysplasia as invasive carcinoma [ 46].

The ELISA-kit flyers indicate that specificity varies between 92.7 and 100% with human sera [ 36- 38], even if some tests can cross-react with Epstein-Barr virus (EBV) and cytomegalovirus (CMV) antibodies, determining false positive results [ 39].

In this context, the rate of determining false positive TARs was minimized in terms of considering only TARs represented by reads above threshold and provided by both sequencing technologies.

This is the case of Hodgkin lymphoma or anaplastic large cells and some high-grade T-cell lymphoma in which diagnostic cells are too scanty to be gated or being too large, they stick to the tubes of the equipment or get broken or lost determining false negative results.

Current computational tools allow the unambiguous identification of more than half of all tandem mass spectra (Cox and Mann 2008), typically verified by stringent community requirements (Bradshaw et al. 2006) and robust techniques for determining false positives (Elias and Gygi 2007).

One cannot use the well-known method of determining false-negative and false-positive rates.

Consistency-based detection uses consistency data from multiple vehicles to determine false information.

Those functions may be used to determine false positive ratio (FPR) and false negative ratio (FNR) functions which determine respectively the probability that an impostor is classified as a target and a target is classified as an impostor for a particular likelihood.

To determine false positive and false negative rates, we set the array data as standard and distinguished between reference call and SNP call dependence on the array data.

We had included one species (the Douc Langur from Indochina) to determine false positives, but we could not consistently determine from the answers whether respondents had misidentified the species or considered it absent in the area.

To generate a list of mRNAs that were consistently enriched by PUMs and hence represent likely targets, we compared association of transcripts from PUM affinity isolations to the mock isolates by unpaired two-class Significance Analysis of Microarrays (SAM) and determined false discovery rates (FDRs) for each array element [45].