ConvexML: Fast and accurate branch length estimation under irreversible mutation models, illustrated through applications to CRISPR/Cas9-based lineage tracing is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2023). On theSindex it has a DataRank of 0.360. It has been cited 10 times.
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Base Score Contribution
0.360
From this paper's citation signal
Citation Network Contribution
0
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Grant: R56-HG013117
NIH
Grant: R01-HG013117
European Union Council
Grant: Tx-phylogeography, 101089213
National Institutes of Health
Grant: 1R56HG013117-01
Scalable Computational Methods for Genealogical Inference: from species level to single cells
European Commission
Grant: 101089213
High throughput phylogeography of tumors: how the tissue environment influences cancer evolution?
NHGRI NIH HHS
Grant: R01 HG013117
NHGRI NIH HHS
Grant: R56 HG013117
Influential Citations
2
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Keywords
ConvexML: Scalable and accurate inference of single-cell chronograms from CRISPR/Cas9 lineage tracing data