2015
DOI: 10.1186/s12859-015-0715-9
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Statistical analysis of a Bayesian classifier based on the expression of miRNAs

Abstract: BackgroundDuring the last decade, many scientific works have concerned the possible use of miRNA levels as diagnostic and prognostic tools for different kinds of cancer. The development of reliable classifiers requires tackling several crucial aspects, some of which have been widely overlooked in the scientific literature: the distribution of the measured miRNA expressions and the statistical uncertainty that affects the parameters that characterize a classifier. In this paper, these topics are analysed in det… Show more

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Cited by 7 publications
(8 citation statements)
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“…It is typically expressed by lung NE tumor (Demelash et al, 2012) and directly transactivates miR375-3p in cell lines and tumors (Nishikawa et al, 2011). In the present study we analyze miR375-3p expression in 95 surgically resected lung tumors, including 31 TC, 11 AT, 11 LCNEC, 4 SCLC, 22 AD, and 16 SQC and we demonstrate that, via an implemented statistical approach which has been recently developed and validated by our group (Ricci et al, 2015), miR375-3p is able to distinguish low-grade NE from non-NE lung tumors, but not LCNEC from SCLC tumors.…”
Section: Introductionmentioning
confidence: 66%
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“…It is typically expressed by lung NE tumor (Demelash et al, 2012) and directly transactivates miR375-3p in cell lines and tumors (Nishikawa et al, 2011). In the present study we analyze miR375-3p expression in 95 surgically resected lung tumors, including 31 TC, 11 AT, 11 LCNEC, 4 SCLC, 22 AD, and 16 SQC and we demonstrate that, via an implemented statistical approach which has been recently developed and validated by our group (Ricci et al, 2015), miR375-3p is able to distinguish low-grade NE from non-NE lung tumors, but not LCNEC from SCLC tumors.…”
Section: Introductionmentioning
confidence: 66%
“…With the exception of 4 misclassified cases, the classification provided by the classifier coincides with the immunohistochemical diagnosis ( Supplementary Table S2). There are also three technical outliers, based on statistical analysis (Ricci et al, 2015), which, however, are correctly classified by Ct 375 . The resulting ROC ( Figure 1D) displayed an AUC equal to 0.88.…”
Section: Tc)mentioning
confidence: 98%
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