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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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1 tweeter

Citations

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9 Dimensions

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20 Mendeley
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.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 10%
Italy 1 5%
Unknown 17 85%

Demographic breakdown

Readers by professional status Count As %
Professor 3 15%
Student > Master 3 15%
Professor > Associate Professor 3 15%
Student > Bachelor 2 10%
Student > Postgraduate 2 10%
Other 7 35%
Readers by discipline Count As %
Medicine and Dentistry 8 40%
Unspecified 3 15%
Agricultural and Biological Sciences 2 10%
Pharmacology, Toxicology and Pharmaceutical Science 1 5%
Mathematics 1 5%
Other 5 25%

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
#2,311,732
of 4,507,072 outputs
Outputs from PLoS ONE
#42,894
of 80,036 outputs
Outputs of similar age
#37,568
of 79,541 outputs
Outputs of similar age from PLoS ONE
#1,954
of 3,716 outputs
Altmetric has tracked 4,507,072 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 80,036 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.3. This one is in the 34th percentile – i.e., 34% 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 79,541 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 3,716 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.