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.
Highlights
A general way to understand complex biological systems is to represent them using the simplest units of architecture
A multitude of studies have shown that meaningful biological properties can be extracted by network analysis [5,6]
The aim of this study is to apply conjunctly statistical analysis with network tools and methodologies to Class III malocclusion features’ longitudinal datasets in order to uncover the systemic importance of such features and to individuate the possible emergence of features’ subset driving the orofacial development of Class III malocclusion
Summary
A general way to understand complex biological systems is to represent them using the simplest units of architecture. Such patterns of local and global interconnection are called networks. A network, or in more formal mathematical language, a graph, is a simplified representation that reduces a system to an abstract structure capturing the basis of connection pattern of the system [1,2]. The simplest possible network representation reduces the system’s elements to nodes (‘‘vertices’’) and their pairwise relationships to links (‘‘edges’’) connecting pairs of nodes. The network’s inference and analysis refers to information on the identity and the state of the elements of a system to their functional relationships and to the extraction of biological insight and predictions. A multitude of studies have shown that meaningful biological properties can be extracted by network analysis [5,6]
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