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. 2008 Jan 4:9:2.
doi: 10.1186/1471-2105-9-2.

Supervised inference of gene-regulatory networks

Cuong C To  1 , Jiri Vohradsky
Affiliations

Supervised inference of gene-regulatory networks

Cuong C To et al. BMC Bioinformatics. .

Abstract

Background: Inference of protein interaction networks from various sources of data has become an important topic of both systems and computational biology. Here we present a supervised approach to identification of gene expression regulatory networks.

Results: The method is based on a kernel approach accompanied with genetic programming. As a data source, the method utilizes gene expression time series for prediction of interactions among regulatory proteins and their target genes. The performance of the method was verified using Saccharomyces cerevisiae cell cycle and DNA/RNA/protein biosynthesis gene expression data. The results were compared with independent data sources. Finally, a prediction of novel interactions within yeast gene expression circuits has been performed.

Conclusion: Results show that our algorithm gives, in most cases, results identical with the independent experiments, when compared with the YEASTRACT database. In several cases our algorithm gives predictions of novel interactions which have not been reported.

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Figures

Figure 1
Figure 1
Principle of prediction of a complete network from a training network (or sub-network, shaded area). Vertices represent proteins, edges represent interactions. The training network contains known connections, while the connections outside the shaded area represent connections predicted by extending the rules for training network to other proteins.
Figure 2
Figure 2
Interaction networks of genes adopted from the work of Lee et al. [6] with sub-networks (bold) used as a training set. Shaded nodes represent genes for which the regulatory interactions were predicted using the algorithm presented here. A – cell cycle network, B – DNA/RNA/protein synthesis network.
Figure 3
Figure 3
Symbolic representation of supervised inference of protein interaction network. Filled area of panel a represent known part of the network to be inferred. b – time series of microarray or proteomic experiment. Both data are mapped onto a common feature space c where the interaction of the proteins is inferred from the known interactions shown in panel a.
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References

    1. Scholkopf B, Smola AJ. Learning with kernels. Cambridge, Massachusets , The MIT Press; 2002.
    1. Wang H, Segal E, Ben-Hur A, Koller D, Brutlag DL. Identifying protein-protein interaction sites on a genome-wide scale. In: LK Saul YWLB, editor. Advaneces in Neural Information Processing Systems 17. Cambridge, MA , MIT press; 2005. pp. 1465–1472.
    1. Ben-Hur A, Noble WS. Kernel methods for predicting protein-protein interactions. Bioinformatics. 2005;21 Suppl 1:i38–46. doi: 10.1093/bioinformatics/bti1016. - DOI - PubMed
    1. Yamanishi Y, Vert JP, Kanehisa M. Protein network inference from multiple genomic data: a supervised approach. Bioinformatics. 2004;20 Suppl 1:I363–I370. doi: 10.1093/bioinformatics/bth910. - DOI - PubMed
    1. Kato T, Tsuda K, Asai K. Selective integration of multiple biological data for supervised network inference. Bioinformatics. 2005;21:2488–2495. doi: 10.1093/bioinformatics/bti339. - DOI - PubMed

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