ZeitZeiger: supervised learning for high-dimensional data from an oscillatory system is a research paper published in Nucleic Acids Research (2016). On theSindex it has a DataRank of 4.2. It has been cited 114 times, with 101 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
0.712
From this paper's citation signal
Citation Network Contribution
3.5
From 81 citing papers with measurable signal
Ranked by each citer's contribution to N(p) — log1p(Cq) divided by its reference count — out of 101 citers.
NIBIB NIH HHS
Grant: R01 EB001988
NLM NIH HHS
Grant: T15 LM007033
NIGMS NIH HHS
Grant: R01 GM079719
National Institutes of Health
Grant: 5R01EB001988-14
New Statistical Methods for Medical Signals and Images
National Institutes of Health
Grant: 5T15LM007033-36
Biomedical Informatics Training at Stanford
National Science Foundation
Grant: 1407548
Flexible Statistical Modeling
National Institutes of Health
Grant: 5R01GM079719-04
Enabling new discoveries in pharmacogenomics through a genomic date-driven nosolo
FWCI
5.09
Citation Percentile
1.0%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
Additional file 1 of A population-based gene expression signature of molecular clock phase from a single epidermal sample
Additional file 1 of A population-based gene expression signature of molecular clock phase from a single epidermal sample
Additional file 1 of PredCRG: A computational method for recognition of plant circadian genes by employing support vector machine with Laplace kernel
Additional file 1 of PredCRG: A computational method for recognition of plant circadian genes by employing support vector machine with Laplace kernel
Additional file 2 of PredCRG: A computational method for recognition of plant circadian genes by employing support vector machine with Laplace kernel
Additional file 2 of PredCRG: A computational method for recognition of plant circadian genes by employing support vector machine with Laplace kernel
Additional file 3 of PredCRG: A computational method for recognition of plant circadian genes by employing support vector machine with Laplace kernel
Additional file 3 of PredCRG: A computational method for recognition of plant circadian genes by employing support vector machine with Laplace kernel
Data from: ZeitZeiger: supervised learning for high-dimensional data from an oscillatory system
Additional file 6 of A population-based gene expression signature of molecular clock phase from a single epidermal sample
Additional file 6 of A population-based gene expression signature of molecular clock phase from a single epidermal sample
Additional file 5 of A population-based gene expression signature of molecular clock phase from a single epidermal sample
Additional file 3 of A population-based gene expression signature of molecular clock phase from a single epidermal sample
Additional file 2 of A population-based gene expression signature of molecular clock phase from a single epidermal sample
Additional file 3 of A population-based gene expression signature of molecular clock phase from a single epidermal sample
Additional file 5 of A population-based gene expression signature of molecular clock phase from a single epidermal sample
Additional file 4 of A population-based gene expression signature of molecular clock phase from a single epidermal sample
Additional file 4 of A population-based gene expression signature of molecular clock phase from a single epidermal sample
Additional file 2 of A population-based gene expression signature of molecular clock phase from a single epidermal sample
Data from: ZeitZeiger: supervised learning for high-dimensional data from an oscillatory system