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A multi-view genomic data simulator

Overview of attention for article published in BMC Bioinformatics, May 2015
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Title
A multi-view genomic data simulator
Published in
BMC Bioinformatics, May 2015
DOI 10.1186/s12859-015-0577-1
Pubmed ID
Authors

Michele Fratello, Angela Serra, Vittorio Fortino, Giancarlo Raiconi, Roberto Tagliaferri, Dario Greco

Abstract

OMICs technologies allow to assay the state of a large number of different features (e.g., mRNA expression, miRNA expression, copy number variation, DNA methylation, etc.) from the same samples. The objective of these experiments is usually to find a reduced set of significant features, which can be used to differentiate the conditions assayed. In terms of development of novel feature selection computational methods, this task is challenging for the lack of fully annotated biological datasets to be used for benchmarking. A possible way to tackle this problem is generating appropriate synthetic datasets, whose composition and behaviour are fully controlled and known a priori. Here we propose a novel method centred on the generation of networks of interactions among different biological molecules, especially involved in regulating gene expression. Synthetic datasets are obtained from ordinary differential equations based models with known parameters. Our results show that the generated datasets are well mimicking the behaviour of real data, for popular data analysis methods are able to selectively identify existing interactions. The proposed method can be used in conjunction to real biological datasets in the assessment of data mining techniques. The main strength of this method consists in the full control on the simulated data while retaining coherence with the real biological processes. The R package MVBioDataSim is freely available to the scientific community at http://neuronelab.unisa.it/?p=1722 .

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 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
United States 1 1%
Sri Lanka 1 1%
France 1 1%
Unknown 64 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 31%
Researcher 12 18%
Student > Master 6 9%
Student > Doctoral Student 5 7%
Professor 5 7%
Other 13 19%
Unknown 6 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 28%
Biochemistry, Genetics and Molecular Biology 14 21%
Computer Science 12 18%
Medicine and Dentistry 7 10%
Engineering 4 6%
Other 6 9%
Unknown 6 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 07 October 2015.
All research outputs
#13,434,323
of 22,803,211 outputs
Outputs from BMC Bioinformatics
#4,194
of 7,281 outputs
Outputs of similar age
#126,889
of 264,485 outputs
Outputs of similar age from BMC Bioinformatics
#76
of 120 outputs
Altmetric has tracked 22,803,211 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,281 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 39th percentile – i.e., 39% 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 264,485 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 50% of its contemporaries.
We're also able to compare this research output to 120 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.