NIMCE

NIMCE infers gene regulatory networks (GRNs) from time-series gene expression data by applying information-theoretic measures, including transfer entropy and causation entropy, across multiple time delays.

Key Features:

• Information-theoretic framework: Employs information-theoretic measures to quantify dependencies in time-series gene expression data.
• Transfer entropy: Quantifies the directional flow of information between pairs of genes to identify potential regulatory links.
• Causation entropy: Filters out indirect relationships to retain direct regulatory connections.
• Multi time delays: Evaluates information transfer across multiple temporal delays to capture temporal dependencies in regulation.
• GRN reconstruction: Reconstructs directed gene regulatory networks (GRNs) from time-series expression datasets.

Scientific Applications:

• Gene regulatory network inference: Infers GRNs to elucidate regulatory relationships among genes from time-series expression data.
• Study of regulatory dynamics: Applied to analyze regulatory processes involved in cell cycle regulation, differentiation, and apoptosis.

Methodology:

Computes transfer entropy between gene expression time series, applies causation entropy to remove indirect interactions, and evaluates information transfer across multiple time delays.