A strategy to incorporate prior knowledge into correlation network cutoff selection
Clicks: 36
ID: 278925
2020
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Emerging Content
10.5
/100
36 views
16 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #476 of 485 articles by views in Nature communications
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 485 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
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.
| Reference Key |
benedetti2020anature
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Benedetti, Elisa;Pučić-Baković, Maja;Keser, Toma;Gerstner, Nathalie;Büyüközkan, Mustafa;Štambuk, Tamara;Selman, Maurice H. J.;Rudan, Igor;Polašek, Ozren;Hayward, Caroline;Al-Amin, Hassen;Suhre, Karsten;Kastenmüller, Gabi;Lauc, Gordan;Krumsiek, Jan; |
| Journal | Nature communications |
| Year | 2020 |
| DOI |
DOI not found
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.