minet

minet infers mutual information networks from microarray and other gene expression datasets to reconstruct gene regulatory interactions and quantify statistical dependencies between genes.

Key Features:

• Implementation: R/Bioconductor package (version 1.1.6) for network inference from gene expression data.
• Entropy Estimators: Provides four entropy estimation methods: Empirical, Miller-Madow, Schurmann-Grassberger, and Shrink.
• Inference Methods: Supports four network inference algorithms: Relevance Networks, ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks), CLR (Context Likelihood of Relatedness), and MRNET (Mutual Information-based Network Estimation).
• Accuracy Assessment Tools: Integrates evaluation metrics including F-scores, precision-recall (PR) curves, and receiver operating characteristic (ROC) curves.

Scientific Applications:

• Transcriptional network reconstruction: Infers gene-to-gene interactions from microarray gene expression data to model transcriptional networks.
• Systems biology and genomics: Characterizes regulatory relationships and statistical dependencies among genes to support systems-level analyses.
• Network benchmarking and validation: Enables comparison and validation of inferred networks against reference networks using F-scores, PR, and ROC analyses.

Methodology:

minet computes mutual information from microarray gene expression datasets using selectable entropy estimators (Empirical, Miller-Madow, Schurmann-Grassberger, Shrink), applies inference algorithms (Relevance Networks, ARACNE, CLR, MRNET) to construct networks, and assesses accuracy with F-scores, precision-recall and ROC curves.