Interpretable neural architecture search and transfer learning for understanding CRISPR/Cas9 off-target enzymatic reactions.

Zijun Zhang, Adam R Lamson, Michael Shelley, Olga Troyanskaya

Abstract readPreprint

In one paragraph

Article in ArXiv, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

Updated by
Interpretable neural architecture search and transfer learning for understanding CRISPR-Cas9 off-target enzymatic reactions.2023

5 · Who and what money

Authors and funding

4 authors.

Zijun ZhangDivision of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, 116 N. Robertson Blvd, Los Angeles, 90048, CA, USA.

Adam R LamsonCenter for Computational Biology, Flatiron Institute, 162 5th Ave, New York City, 10010, NY, USA.

Michael ShelleyCenter for Computational Biology, Flatiron Institute, 162 5th Ave, New York City, 10010, NY, USA.

Olga TroyanskayaCenter for Computational Biology, Flatiron Institute, 162 5th Ave, New York City, 10010, NY, USA.

Funding

lntegration and Visualization of Diverse Biological DataR01GM071966 · NIGMS · PRINCETON UNIVERSITY · PI TROYANSKAYA, OLGA G · 2005 to 2022

$6.4M

NIGMS NIH HHS R01 GM071966

6 · The paper itself

Abstract

Finely-tuned enzymatic pathways control cellular processes, and their dysregulation can lead to disease. Creating predictive and interpretable models for these pathways is challenging because of the complexity of the pathways and of the cellular and genomic contexts. Here we introduce

Indexed as

AutoMLgenome editingInterpretable neural networksneural architecture searchtransfer learning

Identifiers

PMID37808087

PMCPMC10557798

What OpenQuestion holds

Textmetadata

LicenceCC BY

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.