Neural networks to learn protein sequence–function relationships from deep mutational scanning data is a research paper published in Proceedings of the National Academy of Sciences (2021). On theSindex it has a DataRank of 4.5. It has been cited 180 times, with 159 citing works in its 1-hop citation network.
Scored on demand from live citation data
Linked data & code
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
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Base Score Contribution
0.780
From this paper's citation signal
Citation Network Contribution
3.7
From 127 citing papers with measurable signal
Ranked by each citer's contribution to N(p) — log1p(Cq) divided by its reference count — out of 159 citers.
HHS | National Institutes of Health
Grant: R35GM119854
HHS | National Institutes of Health
Grant: R01GM135631
HHS | National Institutes of Health
Grant: T32HG002760
Pharmaceutical Research and Manufacturers of America Foundation
Grant: N/A
National Institutes of Health
Grant: 5R01GM135631-03
A Machine Learning Platform for Adaptive Chemical Screening
National Institutes of Health
Grant: 2T32HG002760-21
Institutional Training in the Genomic Sciences
National Institutes of Health
Grant: 5R35GM119854-02
Data-driven analysis of protein structure, function, and regulation
FWCI
9.28
Citation Percentile
1.0%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
Additional file 1 of MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect
Additional file 1 of MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect
Additional file 1 of MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect
Additional file 2 of MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect
Additional file 2 of MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect
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Additional file 1 of SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering
Additional file 1 of MBE: model-based enrichment estimation and prediction for differential sequencing data
Additional file 1 of MBE: model-based enrichment estimation and prediction for differential sequencing data
Additional file 2 of MBE: model-based enrichment estimation and prediction for differential sequencing data
Additional file 2 of MBE: model-based enrichment estimation and prediction for differential sequencing data
Additional file 2 of Prediction of mutation-induced protein stability changes based on the geometric representations learned by a self-supervised method
Additional file 2 of Prediction of mutation-induced protein stability changes based on the geometric representations learned by a self-supervised method
Additional file 1 of Prediction of mutation-induced protein stability changes based on the geometric representations learned by a self-supervised method
Additional file 1 of Prediction of mutation-induced protein stability changes based on the geometric representations learned by a self-supervised method
Additional file 2 of SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering
Additional file 3 of SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering
Additional file 2 of SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering
Additional file 3 of SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering