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Related Experiment Video

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CN: a consensus algorithm for inferring gene regulatory networks using the SORDER algorithm and conditional mutual

Rosa Aghdam¹, Mojtaba Ganjali, Xiujun Zhang

¹Faculty of Mathematical Sciences, Department of Statistics, Shahid Beheshti University, G.C., Tehran, Iran. n-aghdam@sbu.ac.ir.

Molecular Biosystems

|January 22, 2015

Summary

This summary is machine-generated.

This study introduces the Consensus Network (CN) algorithm to improve gene regulatory network (GRN) inference. CN enhances precision by addressing the order-dependency and threshold sensitivity of the Path Consistency (PC) algorithm.

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Area of Science:

Systems biology
Computational biology
Bioinformatics

Background:

Inferring gene regulatory networks (GRNs) from gene expression data is crucial in systems biology.
The Path Consistency (PC) algorithm is a popular method but suffers from order dependency and threshold sensitivity, impacting GRN inference robustness.
Selecting optimal node ordering and thresholds for PC algorithm remains challenging for accurate GRN reconstruction.

Purpose of the Study:

To develop a robust and precise method for inferring gene regulatory networks.
To overcome the limitations of existing algorithms like the Path Consistency (PC) algorithm.
To introduce a novel heuristic algorithm (SORDER) for node ordering and a consensus network (CN) approach for GRN inference.

Main Methods:

Proposed the SORDER heuristic algorithm to determine a suitable sequential ordering of nodes.
Developed the Consensus Network (CN) method using SORDER and an interval threshold for Conditional Mutual Information (CMI) tests.
Assigned weighted reliability values to edges representing node dependencies in the complete graph.

Main Results:

The Consensus Network (CN) algorithm demonstrated effectiveness in learning GRNs.
Benchmarking on DREAM challenge networks and the E. coli SOS DNA repair network showed significant improvements in inference precision.
The method successfully infers GRNs by selecting edges with reliability values exceeding a defined threshold.

Conclusions:

The developed Consensus Network (CN) algorithm offers a robust approach to gene regulatory network inference.
CN significantly enhances the precision of GRN reconstruction compared to existing methods.
The SORDER algorithm and CN method provide a valuable framework for systems biology research.