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Unfolding and De-confounding: Biologically meaningful cau... | ResearchHub
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Unfolding and De-confounding: Biologically meaningful causal inference from longitudinal multi-omic networks using METALICA
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Authors
Daniel Ruiz-Perez
4 more
Daniel Ruiz-Perez
•
Isabella Gimon
2 more
•
Giri Narasimhan
Published
December 13, 2023
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Preprint Server
Topics
Biology
Bioinformatics
Computer Science
Medicine
Artificial Intelligence
Show all topics
DOI
10.1101/2023.12.12.571384
License
CC-BY-ND
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Supporters
Support the authors with ResearchCoin
Tip RSC
Preprint Server
Topics
Biology
Bioinformatics
Computer Science
Medicine
Artificial Intelligence
Show all topics
DOI
10.1101/2023.12.12.571384
License
CC-BY-ND
Other Formats
PDF
