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Antibiotic Bacteria Interaction: Dataset and Benchmarking.
Chatterjee, S.; Majumdar, A.; Chouzenoux, E.
2024-02-24
bioengineering
10.1101/2024.02.22.581405
bioRxiv
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Withdrawal StatementThe authors have withdrawn their manuscript owing to inability to reproduce the results. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.
Matching journals
●Non-profit
◐University press
○Commercial
The top 9 journals account for 50% of the predicted probability mass.
1
Scientific Reports
○
3612 papers in training set
Top 6%
8.0%
Similar papers in this journal
- Limitations of estimating antibiotic resistance using German hospital consumption data - A comprehensive computational analysis 93%
- Systematic analysis of microorganisms' metabolism for selective targeting 93%
- Major discrepancy between factual antibiotic resistance and consumption in South of France: analysis of 539,037 bacterial strains. 92%
2
Scientific Data
○
209 papers in training set
Top 0.3%
8.0%
Similar papers in this journal
3
PLOS Computational Biology
●
1863 papers in training set
Top 5%
6.8%
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- A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia 91%
- Using machine learning and big data to explore the drug resistance landscape in HIV 91%
- Is it selfish to be filamentous in biofilms? Individual-based modeling links microbial growth strategies with morphology using the new and modular iDynoMiCS 2.0 91%
4
PeerJ
◐
308 papers in training set
Top 0.4%
6.8%
Similar papers in this journal
5
PLOS ONE
●
5266 papers in training set
Top 29%
4.9%
Similar papers in this journal
- Omnicrobe, an open-access database of microbial habitats and phenotypes using a comprehensive text mining and data fusion approach 93%
- ProphET, Prophage Estimation Tool: a standalone prophage sequence prediction tool with self-updating reference database 91%
- Stability of β-lactam antibiotics in bacterial growth media 91%
50% of probability mass above
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.