Sentence examples for blood classifier from inspiring English sources

We have developed a whole blood classifier based on gene expression, age and sex for the assessment of obstructive CAD in non-diabetic patients from a combination of microarray and RT-PCR data derived from studies of patients clinically indicated for invasive angiography.

Receiver operating characteristics analysis was performed and the area under the curve for melanoma and cord blood classifiers was 0.991 and 0.986, respectively.

In this work we describe the development of a validated whole blood based classifier for the assessment of obstructive CAD[ 6].

Public databases were screened extensively for candidate data sets to be used in development of a blood-based response classifier for infliximab treatment in RA.

The main interest in using peripheral blood for potential classifiers of antidepressant, antipsychotic, and mood stabilizer response, as opposed to brain tissue samples, is the relative ease of acquisition and the practical utility of blood samples.

URSA's ontology-aware Bayesian framework aggregates multiple individual classifiers so that classifiers for large terms (such as blood) could help classify related specific small terms (such as T-cell acute lymphoblastic leukemia cell).

The training set was used to construct blood gene expression-based classifier, while the independent test set allowed for subsequent testing of the classifier performance.

The classifier for blood-selective gene prediction showed overall accuracy at 93.29% with MCC = 0.5109 and ROC AUC = 0.9170.

We investigated whether a blood-based gene expression classifier could predict response to a targeted biologic therapy in RA when subjected to statistically rigorous evaluation across multiple patient cohorts.

We also included a classifier for normal blood cells and patient sex to highlight samples with low blast count and to verify the sex of the patients, respectively.

A significant advantage of the current work is the multitude of data that has been employed (clinical, imaging, tissue genomic and blood genomic) whereby a separate classifier has been trained from each source of data, as well as an overall one that combines the individual classifiers.