Inferring biomolecular interaction networks based on convex optimization
Computational Biology and Chemistry
A hybrid Bayesian network learning method for constructing gene networks
Computational Biology and Chemistry
A mathematical program to refine gene regulatory networks
Discrete Applied Mathematics
International Journal of Bioinformatics Research and Applications
Modeling oncology gene pathways network with multiple genotypes and phenotypes via a copula method
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
Improved Bayesian Network inference using relaxed gene ordering
International Journal of Data Mining and Bioinformatics
Learning gene regulatory networks via globally regularized risk minimization
RECOMB-CG'07 Proceedings of the 2007 international conference on Comparative genomics
A probabilistic approach for learning folksonomies from structured data
Proceedings of the fourth ACM international conference on Web search and data mining
An integer optimization approach for reverse engineering of gene regulatory networks
Discrete Applied Mathematics
Selection of interdependent genes via dynamic relevance analysis for cancer diagnosis
Journal of Biomedical Informatics
A novel strategy for plant breeding based on simulations of gene network models
International Journal of Bioinformatics Research and Applications
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Motivation: Bayesian network methods have shown promise in gene regulatory network reconstruction because of their capability of capturing causal relationships between genes and handling data with noises found in biological experiments. The problem of learning network structures, however, is NP hard. Consequently, heuristic methods such as hill climbing are used for structure learning. For networks of a moderate size, hill climbing methods are not computationally efficient. Furthermore, relatively low accuracy of the learned structures may be observed. The purpose of this article is to present a novel structure learning method for gene network discovery. Results: In this paper, we present a novel structure learning method to reconstruct the underlying gene networks from the observational gene expression data. Unlike hill climbing approaches, the proposed method first constructs an undirected network based on mutual information between two nodes and then splits the structure into substructures. The directional orientations for the edges that connect two nodes are then obtained by optimizing a scoring function for each substructure. Our method is evaluated using two benchmark network datasets with known structures. The results show that the proposed method can identify networks that are close to the optimal structures. It outperforms hill climbing methods in terms of both computation time and predicted structure accuracy. We also apply the method to gene expression data measured during the yeast cycle and show the effectiveness of the proposed method for network reconstruction. Contact: xwchen@ku.edu