Prediction of compound synergism from chemical-genetic interactions by machine learning
Clicks: 183
ID: 260954
2015
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
Popular Article
30.0
/100
183 views
19 readers
AI Quality Assessment
Not analyzed
Readership in this journal
PopularRanked #6 of 6 articles by views in cell systems
Most read
Least read
Bar heights use a square-root scale.
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
The structure of genetic interaction networks predicts that, analogous to synthetic lethal interactions between non-essential genes, combinations of compounds with latent activities may exhibit potent synergism. To test this hypothesis, we generated a ...
Abstract Quality Issue:
This abstract appears to be incomplete or contains metadata (33 words).
Try re-searching for a better abstract.
| Reference Key |
tyers2015cellprediction
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Jan Wildenhain, Michaela Spitzer, Sonam Dolma, David Bellows, Nick Jarvik, Rachel White, Marcia Roy, Emma Griffiths, Gerard D. Wright, Mike Tyers;Jan Wildenhain;Michaela Spitzer;Sonam Dolma;David Bellows;Nick Jarvik;Rachel White;Marcia Roy;Emma Griffiths;Gerard D. Wright;Mike Tyers; |
| Journal | cell systems |
| Year | 2015 |
| DOI |
10.1016/j.cels.2015.12.003
|
| 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.