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| Signing of Regulatory Networks (SIREN) algorithm needs two kinds of data for making inference. As the first source, it needs the presence of an interaction network. This network shows the existing linkages among nodes; and can be directed or undirected. As the second source, SIREN algorithm needs transcriptome data of related biological network. With having these, this new algorithm infers the underlying interaction type between each pair of interacting nodes in the network. | The Signing of Regulatory Networks (SIREN) algorithm can infer the regulatory type (positive or negative regulation) of interactions in a known gene regulatory network given corresponding genome-wide gene expression data. |
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| SIREN is an efficient algorithm with low computational complexity; hence, it is applicable to huge biological networks with a large number of genes. It can be considered as a complementary for a wide range of network reconstruction methods which do not provide any information about the nature of interaction type. |
SIREN is an efficient algorithm with low computational complexity; hence, it is applicable to large biological networks. It is a complementary approach for a wide range of network reconstruction methods that do not provide information about the interaction type. |
Signing of Regulatory Networks (SIREN)
Discrimination of Interaction Type in Biological Networks
The Signing of Regulatory Networks (SIREN) algorithm can infer the regulatory type (positive or negative regulation) of interactions in a known gene regulatory network given corresponding genome-wide gene expression data.
SIREN is an efficient algorithm with low computational complexity; hence, it is applicable to large biological networks. It is a complementary approach for a wide range of network reconstruction methods that do not provide information about the interaction type.