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

Watanabe Y, Seno S, Takenaka Y, Matsuda H - BMC Genomics (2012)

Bottom Line: However, two serious problems reduce the effectiveness of current BN methods.We verified the effectiveness of the proposed method for all known gene regulatory networks and their expression profiles.The results demonstrate that this approach can predict regulatory networks with reduced computational time without degrading the solution accuracy compared with the GHC method.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, Osaka, Japan. w-yukito@ist.osaka-u.ac.jp

ABSTRACT

Background: Bayesian networks (BNs) have been widely used to estimate gene regulatory networks. Many BN methods have been developed to estimate networks from microarray data. However, two serious problems reduce the effectiveness of current BN methods. The first problem is that BN-based methods require huge computational time to estimate large-scale networks. The second is that the estimated network cannot have cyclic structures, even if the actual network has such structures.

Results: In this paper, we present a novel BN-based deterministic method with reduced computational time that allows cyclic structures. Our approach generates all the combinational triplets of genes, estimates networks of the triplets by BN, and unites the networks into a single network containing all genes. This method decreases the search space of predicting gene regulatory networks without degrading the solution accuracy compared with the greedy hill climbing (GHC) method. The order of computational time is the cube of number of genes. In addition, the network estimated by our method can include cyclic structures.

Conclusions: We verified the effectiveness of the proposed method for all known gene regulatory networks and their expression profiles. The results demonstrate that this approach can predict regulatory networks with reduced computational time without degrading the solution accuracy compared with the GHC method.

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Related in: MedlinePlus

Flowchart of the algorithm. Circles represent start and end points. Rectangles represent generic processing steps. Diamonds represent decision steps.
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Figure 2: Flowchart of the algorithm. Circles represent start and end points. Rectangles represent generic processing steps. Diamonds represent decision steps.

Mentions: A flowchart of the algorithm can be found in Figure 2.


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

Watanabe Y, Seno S, Takenaka Y, Matsuda H - BMC Genomics (2012)

Flowchart of the algorithm. Circles represent start and end points. Rectangles represent generic processing steps. Diamonds represent decision steps.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3303741&req=5

Figure 2: Flowchart of the algorithm. Circles represent start and end points. Rectangles represent generic processing steps. Diamonds represent decision steps.
Mentions: A flowchart of the algorithm can be found in Figure 2.

Bottom Line: However, two serious problems reduce the effectiveness of current BN methods.We verified the effectiveness of the proposed method for all known gene regulatory networks and their expression profiles.The results demonstrate that this approach can predict regulatory networks with reduced computational time without degrading the solution accuracy compared with the GHC method.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, Osaka, Japan. w-yukito@ist.osaka-u.ac.jp

ABSTRACT

Background: Bayesian networks (BNs) have been widely used to estimate gene regulatory networks. Many BN methods have been developed to estimate networks from microarray data. However, two serious problems reduce the effectiveness of current BN methods. The first problem is that BN-based methods require huge computational time to estimate large-scale networks. The second is that the estimated network cannot have cyclic structures, even if the actual network has such structures.

Results: In this paper, we present a novel BN-based deterministic method with reduced computational time that allows cyclic structures. Our approach generates all the combinational triplets of genes, estimates networks of the triplets by BN, and unites the networks into a single network containing all genes. This method decreases the search space of predicting gene regulatory networks without degrading the solution accuracy compared with the greedy hill climbing (GHC) method. The order of computational time is the cube of number of genes. In addition, the network estimated by our method can include cyclic structures.

Conclusions: We verified the effectiveness of the proposed method for all known gene regulatory networks and their expression profiles. The results demonstrate that this approach can predict regulatory networks with reduced computational time without degrading the solution accuracy compared with the GHC method.

Show MeSH
Related in: MedlinePlus