Parenclitic networks Uncovering new functions in biological data
We introduce a novel method to represent time independent, scalar data sets as complex networks. We apply our method to investigate gene expression in the response to osmotic stress of Arabidopsis thaliana. In the proposed network representation, the most important genes for the plant response turn out to be the nodes with highest centrality in appropriately reconstructed networks. We also performed a target experiment, in which the predicted genes were artificially induced one by one, and the growth of the corresponding phenotypes compared to that of the wild-type. The joint application of the network reconstruction method and of the in vivo experiments allowed identifying 15 previously unknown key genes, and provided models of their mutual relationships. This novel representation extends the use of graph theory to data sets hitherto considered outside of the realm of its application, vastly simplifying the characterization of their underlying structure.
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2014
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| Online Access: | http://hdl.handle.net/20.500.12792/4792 |
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dig-inia-es-20.500.12792-47922020-12-15T09:54:59Z Parenclitic networks Uncovering new functions in biological data Zanin, M. Medina Alcazar, J. Vicente Carbajosa, J. Gomez Paez, M. Papo, D. Sousa, P. Menasalvas, E. Boccaletti, S. We introduce a novel method to represent time independent, scalar data sets as complex networks. We apply our method to investigate gene expression in the response to osmotic stress of Arabidopsis thaliana. In the proposed network representation, the most important genes for the plant response turn out to be the nodes with highest centrality in appropriately reconstructed networks. We also performed a target experiment, in which the predicted genes were artificially induced one by one, and the growth of the corresponding phenotypes compared to that of the wild-type. The joint application of the network reconstruction method and of the in vivo experiments allowed identifying 15 previously unknown key genes, and provided models of their mutual relationships. This novel representation extends the use of graph theory to data sets hitherto considered outside of the realm of its application, vastly simplifying the characterization of their underlying structure. 2020-10-22T18:05:31Z 2020-10-22T18:05:31Z 2014 journal article http://hdl.handle.net/20.500.12792/4792 10.1038/srep05112 eng Attribution-NonCommercial-ShareAlike 4.0 International http://creativecommons.org/licenses/by-nc-sa/4.0/ open access |
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biblioteca |
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| language |
eng |
| description |
We introduce a novel method to represent time independent, scalar data sets as complex networks. We apply our method to investigate gene expression in the response to osmotic stress of Arabidopsis thaliana. In the proposed network representation, the most important genes for the plant response turn out to be the nodes with highest centrality in appropriately reconstructed networks. We also performed a target experiment, in which the predicted genes were artificially induced one by one, and the growth of the corresponding phenotypes compared to that of the wild-type. The joint application of the network reconstruction method and of the in vivo experiments allowed identifying 15 previously unknown key genes, and provided models of their mutual relationships. This novel representation extends the use of graph theory to data sets hitherto considered outside of the realm of its application, vastly simplifying the characterization of their underlying structure. |
| format |
journal article |
| author |
Zanin, M. Medina Alcazar, J. Vicente Carbajosa, J. Gomez Paez, M. Papo, D. Sousa, P. Menasalvas, E. Boccaletti, S. |
| spellingShingle |
Zanin, M. Medina Alcazar, J. Vicente Carbajosa, J. Gomez Paez, M. Papo, D. Sousa, P. Menasalvas, E. Boccaletti, S. Parenclitic networks Uncovering new functions in biological data |
| author_facet |
Zanin, M. Medina Alcazar, J. Vicente Carbajosa, J. Gomez Paez, M. Papo, D. Sousa, P. Menasalvas, E. Boccaletti, S. |
| author_sort |
Zanin, M. |
| title |
Parenclitic networks Uncovering new functions in biological data |
| title_short |
Parenclitic networks Uncovering new functions in biological data |
| title_full |
Parenclitic networks Uncovering new functions in biological data |
| title_fullStr |
Parenclitic networks Uncovering new functions in biological data |
| title_full_unstemmed |
Parenclitic networks Uncovering new functions in biological data |
| title_sort |
parenclitic networks uncovering new functions in biological data |
| publishDate |
2014 |
| url |
http://hdl.handle.net/20.500.12792/4792 |
| work_keys_str_mv |
AT zaninm parencliticnetworksuncoveringnewfunctionsinbiologicaldata AT medinaalcazarj parencliticnetworksuncoveringnewfunctionsinbiologicaldata AT vicentecarbajosaj parencliticnetworksuncoveringnewfunctionsinbiologicaldata AT gomezpaezm parencliticnetworksuncoveringnewfunctionsinbiologicaldata AT papod parencliticnetworksuncoveringnewfunctionsinbiologicaldata AT sousap parencliticnetworksuncoveringnewfunctionsinbiologicaldata AT menasalvase parencliticnetworksuncoveringnewfunctionsinbiologicaldata AT boccalettis parencliticnetworksuncoveringnewfunctionsinbiologicaldata |
| _version_ |
1758004911127134208 |