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Other ORP type . 2022
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Other ORP type . 2022
License: CC BY
Data sources: Datacite
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Rank-to-Group score: Hierarchical confounder discovery in the experiment–machine learning cycle

appsOther research productkeyboard_double_arrow_right Other ORP type 21 Jan 2022Publisher:Zenodo
Authors: Alex Rogozhnikov; Pavan Ramkumar; Rishi Bedi; Saul Kato; G. Sean Escola;

doi: 10.5281/zenodo.5893468 , 10.5281/zenodo.5893469

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

Abstract

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

Keywords

confounder analysis, dataset, software, python

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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