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Supervised inference of gene-regulatory networks.

To CC, Vohradsky J - BMC Bioinformatics (2008)

Bottom Line: The results were compared with independent data sources.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Laboratory of Bioinformatics, Institute of Microbiology ASCR, Prague, Czech Republic. cuongto@biomed.cas.cz

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.

Show MeSH
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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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.

Mentions: The supervised approach to protein interaction prediction suggested by Yamanishi et al.[4] and used also here, is illustrated in Figure 3. We would like to infer missing network structure (Figure 3a) from experimental time series (Figure 3b), when we already know a part of the network. This situation is depicted in Figure 3a, where we assume that part of the interaction network of n proteins is known. This network can be characterized by a diffusion kernel.


Supervised inference of gene-regulatory networks.

To CC, Vohradsky J - BMC Bioinformatics (2008)

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.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2266705&req=5

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.
Mentions: The supervised approach to protein interaction prediction suggested by Yamanishi et al.[4] and used also here, is illustrated in Figure 3. We would like to infer missing network structure (Figure 3a) from experimental time series (Figure 3b), when we already know a part of the network. This situation is depicted in Figure 3a, where we assume that part of the interaction network of n proteins is known. This network can be characterized by a diffusion kernel.

Bottom Line: The results were compared with independent data sources.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Laboratory of Bioinformatics, Institute of Microbiology ASCR, Prague, Czech Republic. cuongto@biomed.cas.cz

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.

Show MeSH