A Study on Risk Factors Associated with Gestational Diabetes Mellitus.
Description
This dataset contains the complete adjacency matrix and Python scripts used to construct the directed network (DMG network) and compute its topological metrics. The network represents clinical, biochemical, and behavioral variables associated with Gestational Diabetes Mellitus (GDM), based on statistically significant correlations (p < 0.05) reported in the literature. The adjacency matrix is presented in binary format (1 = presence of a directed connection; 0 = absence) and follows a structure where rows and columns correspond to the same ordered set of variables. The Python scripts (compatible with Python 3.x) include code for network construction, visualization, and structural analyses, such as: Degree, closeness, betweenness, and eigenvector centrality. k-core decomposition (7-core). Minimum Dominating Set (MDS) identification via Integer Linear Programming (ILP). All files are provided without access restrictions and are intended to support reproducibility and further research in network-based approaches to clinical epidemiology.
Files
Steps to reproduce
Download the adjacency matrix file and the Python scripts from this repository. Install the required Python libraries: networkx, pandas, numpy, matplotlib. Load the adjacency matrix into Python using pandas.read_csv() or similar. Construct the network using networkx.from_pandas_adjacency() or equivalent function. Calculate the topological metrics (closeness centrality, betweenness centrality, eigenvector centrality, k-core, and minimum dominating set) using networkx functions. Visualize the network using matplotlib and color-code edges by correlation type (positive or negative). Compare the results with those reported in the associated research article.
Institutions
- University of Miami Miller School of Medicine Palm Beach Regional Campus
- Universidad Autonoma de Guerrero
- Benemerita Universidad Autonoma de Puebla