An estimation method for inference of gene regulatory net-work using Bayesian network with uniting of partial...

Area of Science:

• Computational Biology
• Bioinformatics
• Systems Biology

Background:

• Bayesian networks (BNs) are prevalent for gene regulatory network inference from microarray data.
• Current BN methods face challenges with extensive computational time for large networks.
• Existing methods cannot represent cyclic structures present in actual gene regulatory networks.

Purpose of the Study:

• To develop a novel Bayesian network-based deterministic method for gene regulatory network inference.
• To address limitations of existing methods regarding computational efficiency and handling cyclic structures.
• To improve the accuracy and speed of large-scale gene regulatory network prediction.

Main Methods:

• A novel deterministic Bayesian network approach is proposed.
• The method involves generating all gene triplets, estimating their networks, and integrating them.
• This reduces the search space for network prediction compared to greedy hill climbing (GHC).

Main Results:

• The proposed method significantly reduces computational time, with complexity cubic to the number of genes.
• The method successfully incorporates cyclic structures into the estimated gene regulatory networks.
• Network prediction accuracy is maintained compared to the GHC method.

Conclusions:

• The effectiveness of the proposed method was validated using known gene regulatory networks and expression profiles.
• The approach offers a computationally efficient alternative for gene regulatory network inference.
• It provides accurate predictions while accommodating cyclic network topologies.