Sentence examples for microarray diagnosis to from inspiring English sources

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The experiments described below apply microarray diagnosis to agriculture.

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This method takes advantage of the highly accurate control of fluids in microchannels, and is superior to DNA microarray diagnosis methods due to its simplicity, highly quantitative determination, and high-sensitivity.

The microarray diagnosis for the polar body pairs and the embryo biopsy are listed in Table  I.

Firstly, the results for each classification built from training datasets are expressed in terms of a classification accuracy rate, which represents the similarity between the pathological clinical diagnosis and the microarray diagnosis [ 14].

Gene microarray data have contributed greatly to pathogenesis and to the identification of biomarkers for diagnosis, to patient stratification and prognostication of RA [10].

Specifically, gene microarray data have contributed greatly to pathogenesis and to the identification of biomarkers for diagnosis, to patient stratification and prognostication of RA [ 12].

Many researchers have explored the use of microarray technology to build cancer diagnosis, prognosis, and prediction classifiers, since the pioneering work of Golub et al. in applying gene expression monitoring by DNA microarray to cancer classification [ 2].

With the use of microarrays including clinical diagnosis, genetic susceptibility to disease and treatment, the need to determine the potential role of inter-individual variation on gene expression patterns in all potentially exposed tissues.

In this study, we carried out a genome-wide microRNA (miRNA) microarray analysis to determine the miRNA expression profiles and relapse-associated miRNA patterns in a panel of matched diagnosis relapse or diagnosis complete remission (CR) childhood ALL samples.

Expression microarray technology promises to change phenotypic characterization of tumors, leading to better diagnosis, prognosis, and ultimately treatment of cancer.

In general, specifically tailored macroarrays are more suitable than microarrays for diagnosis, drug discovery and validation, and for prognostic assessment of clinical treatments due to their low levels of background noise, flexibility, and lower price [ 1].

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