iDIRECT

iDIRECT infers direct dependencies within association networks using copula-based transitivity to disentangle direct from indirect interactions.

Key Features:

• Copula-Based Transitivity: Models transitive relationships with copulas to distinguish direct from indirect interactions.
• Mathematical Robustness: Mitigates ill-conditioning, self-looping, and interaction strength overflow to stabilize network inference.
• High Prediction Accuracy: Demonstrates superior prediction accuracy in simulation benchmarks and outperformed other methods in the DREAM5 Network Inference Challenge for Escherichia coli.

Scientific Applications:

• Gene Regulatory Networks: Reconstructs gene regulatory networks and improved performance was observed on DREAM5 benchmarking for Escherichia coli.
• Ecological Studies: Applied to grassland soil microbial communities under climate warming, yielding networks with fewer nodes and links but increased relative modularity.
• Complexity and Robustness Analysis: Reveals increased complexity under warming and greater resilience of iDIRECT-processed networks to random and targeted species removal.

Methodology:

Applies copula-based transitivity to separate direct from indirect interactions and mitigates ill-conditioning, self-looping, and interaction strength overflow.