A strategy to incorporate prior knowledge into correlation network cutoff selection
Correlation network inference is typically based on the significance of the correlation coefficients, but this procedure is not guaranteed to capture biological mechanisms. Here, the authors develop a cutoff selection algorithm that maximizes the overlap between inferred networks and prior knowledge...
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Nature Publishing Group
2020-10-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-020-18675-3 |
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doaj-010a6cf3af164aa7978eb3f55d61f7d72021-05-11T08:44:05ZengNature Publishing GroupNature Communications2041-17232020-10-0111111210.1038/s41467-020-18675-3A strategy to incorporate prior knowledge into correlation network cutoff selectionElisa Benedetti0Maja Pučić-Baković1Toma Keser2Nathalie Gerstner3Mustafa Büyüközkan4Tamara Štambuk5Maurice H. J. Selman6Igor Rudan7Ozren Polašek8Caroline Hayward9Hassen Al-Amin10Karsten Suhre11Gabi Kastenmüller12Gordan Lauc13Jan Krumsiek14Institute of Computational Biology, Helmholtz Center Munich - German Research Center for Environmental HealthGenos Glycoscience Research LaboratoryFaculty of Pharmacy and Biochemistry, University of ZagrebInstitute of Computational Biology, Helmholtz Center Munich - German Research Center for Environmental HealthInstitute of Computational Biology, Helmholtz Center Munich - German Research Center for Environmental HealthGenos Glycoscience Research LaboratoryLeiden University Medical CenterUsher Institute of Population Health Sciences and Informatics, University of EdinburghUniversity of Split School of MedicineMedical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of EdinburghDepartment of Psychiatry, Weill Cornell Medicine in QatarDepartment of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Education CityInstitute of Computational Biology, Helmholtz Center Munich - German Research Center for Environmental HealthGenos Glycoscience Research LaboratoryInstitute of Computational Biology, Helmholtz Center Munich - German Research Center for Environmental HealthCorrelation network inference is typically based on the significance of the correlation coefficients, but this procedure is not guaranteed to capture biological mechanisms. Here, the authors develop a cutoff selection algorithm that maximizes the overlap between inferred networks and prior knowledge.https://doi.org/10.1038/s41467-020-18675-3 |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Elisa Benedetti Maja Pučić-Baković Toma Keser Nathalie Gerstner Mustafa Büyüközkan Tamara Štambuk Maurice H. J. Selman Igor Rudan Ozren Polašek Caroline Hayward Hassen Al-Amin Karsten Suhre Gabi Kastenmüller Gordan Lauc Jan Krumsiek |
| spellingShingle |
Elisa Benedetti Maja Pučić-Baković Toma Keser Nathalie Gerstner Mustafa Büyüközkan Tamara Štambuk Maurice H. J. Selman Igor Rudan Ozren Polašek Caroline Hayward Hassen Al-Amin Karsten Suhre Gabi Kastenmüller Gordan Lauc Jan Krumsiek A strategy to incorporate prior knowledge into correlation network cutoff selection Nature Communications |
| author_facet |
Elisa Benedetti Maja Pučić-Baković Toma Keser Nathalie Gerstner Mustafa Büyüközkan Tamara Štambuk Maurice H. J. Selman Igor Rudan Ozren Polašek Caroline Hayward Hassen Al-Amin Karsten Suhre Gabi Kastenmüller Gordan Lauc Jan Krumsiek |
| author_sort |
Elisa Benedetti |
| title |
A strategy to incorporate prior knowledge into correlation network cutoff selection |
| title_short |
A strategy to incorporate prior knowledge into correlation network cutoff selection |
| title_full |
A strategy to incorporate prior knowledge into correlation network cutoff selection |
| title_fullStr |
A strategy to incorporate prior knowledge into correlation network cutoff selection |
| title_full_unstemmed |
A strategy to incorporate prior knowledge into correlation network cutoff selection |
| title_sort |
strategy to incorporate prior knowledge into correlation network cutoff selection |
| publisher |
Nature Publishing Group |
| series |
Nature Communications |
| issn |
2041-1723 |
| publishDate |
2020-10-01 |
| description |
Correlation network inference is typically based on the significance of the correlation coefficients, but this procedure is not guaranteed to capture biological mechanisms. Here, the authors develop a cutoff selection algorithm that maximizes the overlap between inferred networks and prior knowledge. |
| url |
https://doi.org/10.1038/s41467-020-18675-3 |
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