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Using Networks To Understand Medical Data: The Case of Class III Malocclusions

Overview of attention for article published in PLOS ONE, September 2012
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Title
Using Networks To Understand Medical Data: The Case of Class III Malocclusions
Published in
PLOS ONE, September 2012
DOI 10.1371/journal.pone.0044521
Pubmed ID
Authors

Antonio Scala, Pietro Auconi, Marco Scazzocchio, Guido Caldarelli, James A. McNamara, Lorenzo Franchi

Abstract

A system of elements that interact or regulate each other can be represented by a mathematical object called a network. While network analysis has been successfully applied to high-throughput biological systems, less has been done regarding their application in more applied fields of medicine; here we show an application based on standard medical diagnostic data. We apply network analysis to Class III malocclusion, one of the most difficult to understand and treat orofacial anomaly. We hypothesize that different interactions of the skeletal components can contribute to pathological disequilibrium; in order to test this hypothesis, we apply network analysis to 532 Class III young female patients. The topology of the Class III malocclusion obtained by network analysis shows a strong co-occurrence of abnormal skeletal features. The pattern of these occurrences influences the vertical and horizontal balance of disharmony in skeletal form and position. Patients with more unbalanced orthodontic phenotypes show preponderance of the pathological skeletal nodes and minor relevance of adaptive dentoalveolar equilibrating nodes. Furthermore, by applying Power Graphs analysis we identify some functional modules among orthodontic nodes. These modules correspond to groups of tightly inter-related features and presumably constitute the key regulators of plasticity and the sites of unbalance of the growing dentofacial Class III system. The data of the present study show that, in their most basic abstraction level, the orofacial characteristics can be represented as graphs using nodes to represent orthodontic characteristics, and edges to represent their various types of interactions. The applications of this mathematical model could improve the interpretation of the quantitative, patient-specific information, and help to better targeting therapy. Last but not least, the methodology we have applied in analyzing orthodontic features can be applied easily to other fields of the medical science.

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Mendeley readers

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The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 2 5%
Germany 2 5%
Unknown 40 91%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 16%
Researcher 5 11%
Student > Postgraduate 4 9%
Professor 4 9%
Student > Ph. D. Student 4 9%
Other 12 27%
Unknown 8 18%
Readers by discipline Count As %
Medicine and Dentistry 18 41%
Biochemistry, Genetics and Molecular Biology 2 5%
Psychology 2 5%
Physics and Astronomy 2 5%
Agricultural and Biological Sciences 2 5%
Other 7 16%
Unknown 11 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 24 September 2012.
All research outputs
#15,251,976
of 22,679,690 outputs
Outputs from PLOS ONE
#129,864
of 193,573 outputs
Outputs of similar age
#107,207
of 170,728 outputs
Outputs of similar age from PLOS ONE
#2,724
of 4,259 outputs
Altmetric has tracked 22,679,690 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 193,573 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.0. This one is in the 24th percentile – i.e., 24% of its peers scored the same or lower than it.
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We're also able to compare this research output to 4,259 others from the same source and published within six weeks on either side of this one. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.