Deep learning of the tissue-regulated splicing code is a research paper published in Bioinformatics (2014). On theSindex it has a DataRank of 0.928. It has been cited 485 times.
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Base Score Contribution
0.928
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 →Canadian Institutes of Health Research
Grant: unidentified
unidentified
Canadian Institutes of Health Research
FWCI
13.93
Citation Percentile
1.0%
Influential Citations
9
Citation Trend
Fields of Study
MeSH Terms
Keywords
Additional file 1 of Deep learning-based cancer survival prognosis from RNA-seq data: approaches and evaluations
Additional file 1 of Deep learning-based cancer survival prognosis from RNA-seq data: approaches and evaluations
Additional file 3 of Splicing complexity as a pivotal feature of alternative exons in mammalian species
Additional file 3 of Splicing complexity as a pivotal feature of alternative exons in mammalian species
Additional file 1 of Improved modeling of RNA-binding protein motifs in an interpretable neural model of RNA splicing
Additional file 1 of Improved modeling of RNA-binding protein motifs in an interpretable neural model of RNA splicing
Additional file 2 of Improved modeling of RNA-binding protein motifs in an interpretable neural model of RNA splicing
Additional file 2 of Improved modeling of RNA-binding protein motifs in an interpretable neural model of RNA splicing
Additional file 2 of Splicing complexity as a pivotal feature of alternative exons in mammalian species
Additional file 1 of Splicing complexity as a pivotal feature of alternative exons in mammalian species
Additional file 1 of Splicing complexity as a pivotal feature of alternative exons in mammalian species
Additional file 2 of Splicing complexity as a pivotal feature of alternative exons in mammalian species