CausalR

CausalR infers causal relationships and reconstructs regulatory networks from high-dimensional genomic and molecular datasets to enable causal reasoning within biological networks.

Key Features:

• Causal inference: Leverages advanced statistical techniques to infer causality rather than report only correlations.
• Network prediction: Predicts regulatory interactions and reconstructs regulatory networks from high-dimensional datasets.
• Regulator identification: Identifies potential regulators within biological pathways and network contexts.
• Integration with Bioconductor: Integrates with Bioconductor and the R environment for analysis of high-throughput genomic data.
• Support for genomic data types: Operates on data typical of gene expression and epigenetic studies and other high-dimensional molecular datasets.
• Mechanistic interpretation: Reconstructs networks to elucidate mechanisms of gene regulation and molecular interactions.

Scientific Applications:

• Gene expression analysis: Infers causal regulators and regulatory networks from gene expression datasets.
• Epigenetic research: Applies causal reasoning to epigenetic data to uncover regulatory relationships affecting gene regulation.
• Systems biology and network discovery: Supports reconstruction of pathway and network models for systems-level interpretation.
• Disease mechanism and target discovery: Aids identification of putative regulators and mechanisms relevant to disease and therapeutic target hypotheses.

Methodology:

Uses advanced statistical techniques to infer causality from high-dimensional genomic and molecular datasets and to predict and reconstruct regulatory networks, implemented via integration with Bioconductor in R.