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Statistical analysis of a Bayesian classifier based on the expression of miRNAs

Overview of attention for article published in BMC Bioinformatics, September 2015
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  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

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Title
Statistical analysis of a Bayesian classifier based on the expression of miRNAs
Published in
BMC Bioinformatics, September 2015
DOI 10.1186/s12859-015-0715-9
Pubmed ID
Authors

Leonardo Ricci, Valerio Del Vescovo, Chiara Cantaloni, Margherita Grasso, Mattia Barbareschi, Michela Alessandra Denti

Abstract

During 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 detail by discussing a model problem, i.e. the development of a Bayesian classifier that, on the basis of the expression of miR-205, miR-21 and snRNA U6, discriminates samples into two classes of pulmonary tumors: adenocarcinomas and squamous cell carcinomas. We proved that the variance of miRNA expression triplicates is well described by a normal distribution and that triplicate averages also follow normal distributions. We provide a method to enhance a classifiers' performance by exploiting the correlations between the class-discriminating miRNA and the expression of an additional normalized miRNA. By exploiting the normal behavior of triplicate variances and averages, invalid samples (outliers) can be identified by checking their variability via chi-square test or their displacement by the respective population mean via Student's t-test. Finally, the normal behavior allows to optimally set the Bayesian classifier and to determine its performance and the related uncertainty.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 5%
Italy 1 5%
Unknown 19 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 19%
Student > Ph. D. Student 3 14%
Lecturer 2 10%
Student > Master 2 10%
Student > Doctoral Student 1 5%
Other 5 24%
Unknown 4 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 33%
Biochemistry, Genetics and Molecular Biology 5 24%
Computer Science 3 14%
Pharmacology, Toxicology and Pharmaceutical Science 1 5%
Psychology 1 5%
Other 0 0%
Unknown 4 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 March 2017.
All research outputs
#12,935,224
of 22,826,360 outputs
Outputs from BMC Bioinformatics
#3,788
of 7,287 outputs
Outputs of similar age
#118,268
of 267,016 outputs
Outputs of similar age from BMC Bioinformatics
#54
of 125 outputs
Altmetric has tracked 22,826,360 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,287 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 45th percentile – i.e., 45% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 267,016 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.
We're also able to compare this research output to 125 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.