Result: Rank-to-Group score: Hierarchical confounder discovery in the experiment–machine learning cycle

Title:
Rank-to-Group score: Hierarchical confounder discovery in the experiment–machine learning cycle
Authors:
Alex Rogozhnikov, Pavan Ramkumar, Rishi Bedi, Saul Kato, G. Sean Escola
Publisher Information:
Zenodo
Publication Year:
2022
Collection:
Zenodo
Subject Terms:
confounder analysis, dataset, software, python
Document Type:
other/unknown material
Language:
unknown
Relation:
https://zenodo.org/records/5893469; oai:zenodo.org:5893469; https://doi.org/10.5281/zenodo.5893469
DOI:
10.5281/zenodo.5893469
Availability:
https://doi.org/10.5281/zenodo.5893469
https://zenodo.org/records/5893469
Rights:
Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode
Accession Number:
edsbas.F9F5D7BE
Database:
BASE

Further Information

Data and code for the paper "Hierarchical confounder discovery in the experiment–machine learning cycle". Data is provided as parquet files, and analysis is done with jupyter notebooks using Python 3. We also use data provided by Recursion pharmaceutics. This data is available at https://storage.googleapis.com/rxrx/rxrx2/rxrx2-metadata.zip https://storage.googleapis.com/rxrx/rxrx2/rxrx2-dl-embeddings.zip We provide source code of our utility in open repository https://github.com/herophilus/rtg_score