SEMgraph

SEMgraph performs causal network inference from high-throughput and heterogeneous molecular datasets using structural equation models within the R environment.

Key Features:

• Automated model construction and evaluation: Automated construction and evaluation of multivariate network models representing complex biological systems.
• Data-driven model evaluation: Employs data-driven methods to evaluate model architecture and support robust, reproducible inference.
• Causal inference framework: Integrates network analysis with causal inference to estimate and interpret causal effects among system components.
• Handling heterogeneous data: Integrates large volumes of heterogeneous data from diverse experimental platforms typical of high-throughput studies.
• Reproducibility and interpretability: Produces statistically rigorous outputs that quantify causal effects and facilitate interpretable results.

Scientific Applications:

• Molecular Biology: Modeling gene regulatory networks, protein interactions, and metabolic pathways from high-throughput molecular data.
• Medicine: Studying disease mechanisms and identifying potential therapeutic targets by elucidating causal links between biomarkers and clinical outcomes.

Methodology:

Represents systems as multivariate networks using structural equation models (SEM), integrates network analysis with causal inference, and applies data-driven model selection and evaluation for heterogeneous high-throughput datasets.