monaLisa: an R/Bioconductor package for identifying regulatory motifs is a research paper published in Bioinformatics (2022). On theSindex it has a DataRank of 2.2. It has been cited 120 times, with 106 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?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
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Base Score Contribution
0.719
From this paper's citation signal
Citation Network Contribution
1.5
From 75 citing papers with measurable signal
Ranked by each citer's contribution to N(p) — log1p(Cq) divided by its reference count — out of 106 citers.
European Research Council under the European Union’s
Grant: DNAaccess-884664
Swiss National Science Foundation
Grant: 31003A_149573
Swiss National Science Foundation
Grant: 149573
Swiss National Science Foundation
Grant: 176394
European Research Council
Grant: 884664
Swiss National Science Foundation
Grant: 31003A_175776
European Research Council
Grant: 667951
European Research Council under the European Union’s Horizon 2020 research and innovation programme
Grant: 810111-EpiCrest2Reg
Swiss National Science Foundation
Grant: 310030B_176394
European Research Council under the European Union’s
Grant: ReadMe-667951
Swiss National Science Foundation
Grant: 175776
European Research Council
Grant: 810111
Novartis Research Foundation
FWCI
8.31
Citation Percentile
1.0%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
Additional file 1 of A low-input high resolution sequential chromatin immunoprecipitation method captures genome-wide dynamics of bivalent chromatin
Additional file 1 of A low-input high resolution sequential chromatin immunoprecipitation method captures genome-wide dynamics of bivalent chromatin
Additional file 3 of Kidney-specific methylation patterns correlate with kidney function and are lost upon kidney disease progression
Additional file 3 of Kidney-specific methylation patterns correlate with kidney function and are lost upon kidney disease progression
Additional file 2 of Kidney-specific methylation patterns correlate with kidney function and are lost upon kidney disease progression
Additional file 2 of Kidney-specific methylation patterns correlate with kidney function and are lost upon kidney disease progression
Additional file 1 of Kidney-specific methylation patterns correlate with kidney function and are lost upon kidney disease progression
Additional file 1 of Kidney-specific methylation patterns correlate with kidney function and are lost upon kidney disease progression
Additional file 3 of A low-input high resolution sequential chromatin immunoprecipitation method captures genome-wide dynamics of bivalent chromatin
Additional file 3 of A low-input high resolution sequential chromatin immunoprecipitation method captures genome-wide dynamics of bivalent chromatin
Additional file 2 of A low-input high resolution sequential chromatin immunoprecipitation method captures genome-wide dynamics of bivalent chromatin
Additional file 2 of A low-input high resolution sequential chromatin immunoprecipitation method captures genome-wide dynamics of bivalent chromatin