Network-principled deep generative models for designing drug combinations as graph sets is a research paper published in Bioinformatics (2020). On theSindex it has a DataRank of 0.564. It has been cited 42 times.
Scored on demand from live citation data
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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
0.564
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: R35GM124952
National Institutes of Health
Grant: 5R35GM124952-05
Unraveling molecular and system-level mechanisms of human disease-associated protein mutations
National Institute of General Medical Sciences
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Sustainable Development Goals
Additional file 1 of A benchmark study of deep learning-based multi-omics data fusion methods for cancer
Additional file 1 of A benchmark study of deep learning-based multi-omics data fusion methods for cancer
Additional file 2 of A benchmark study of deep learning-based multi-omics data fusion methods for cancer
Additional file 2 of A benchmark study of deep learning-based multi-omics data fusion methods for cancer