Deep Learning and Computer Vision Strategies for Automated Gene Editing with a Single-Cell Electroporation Platform is a research paper published in SLAS TECHNOLOGY (2021). On theSindex it has a DataRank of 0.434. It has been cited 17 times.
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Linked data & code
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Base Score Contribution
0.434
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →National Institutes of Health
Grant: 1R43GM128500-01
High-throughput single-cell transfection system for gene editing and isogenic iPS cell line generation
National Institute of General Medical Sciences
Grant: 1R21GM132709-01
NIGMS NIH HHS
Grant: R21 GM132709
NIGMS NIH HHS
Grant: R43 GM128500
FWCI
2.13
Citation Percentile
0.9%
Influential Citations
1
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
sj-pdf-1-jla-10.1177_2472630320982320 – Supplemental material for Deep Learning and Computer Vision Strategies for Automated Gene Editing with a Single-Cell Electroporation Platform
sj-pdf-1-jla-10.1177_2472630320982320 – Supplemental material for Deep Learning and Computer Vision Strategies for Automated Gene Editing with a Single-Cell Electroporation Platform
Deep Learning and Computer Vision Strategies for Automated Gene Editing with a Single-Cell Electroporation Platform
Deep Learning and Computer Vision Strategies for Automated Gene Editing with a Single-Cell Electroporation Platform