Knowledge Network Embedding of Transcriptomic Data from Spaceflown Mice Uncovers Signs and Symptoms Associated with Terrestrial Diseases
Knowledge Network Embedding of Transcriptomic Data from Spaceflown Mice Uncovers Signs and Symptoms Associated with Terrestrial Diseases is a dataset published in Life (2021). On theSindex it has a DataRank of 1.0, placing it in the top 41.4% of the data-sharing corpus. It has been cited 20 times, with 8 citing works in its 1-hop citation network. Its calibrated FAIR score is 39/100.
Abstract
There has long been an interest in understanding how the hazards from spaceflight may trigger or exacerbate human diseases. With the goal of advancing our knowledge on physiological changes during space travel, NASA GeneLab provides an open-source repository of multi-omics data from real and simulated spaceflight studies. Alone, this data enables identification of biological changes during spaceflight, but cannot infer how that may impact an astronaut at the phenotypic level. To bridge this gap, Scalable Precision Medicine Oriented Knowledge Engine (SPOKE), a heterogeneous knowledge graph connecting biological and clinical data from over 30 databases, was used in combination with GeneLab transcriptomic data from six studies. This integration identified critical symptoms and physiological changes incurred during spaceflight.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
- Dataset classification
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
DataRank Breakdown
Base Score Contribution
0.449
From this paper's citation signal
Citation Network Contribution
0.596
From 7 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2Genome Biology201497,097 citationsDataRank 1.7
- STAR: ultrafast universal RNA-seq alignerBioinformatics201355,202 citationsDataRank 1.6
- RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genomeBMC Bioinformatics201123,145 citationsDataRank 1.5
- On a Test of Whether one of Two Random Variables is Stochastically Larger than the OtherThe Annals of Mathematical Statistics194713,652 citationsDataRank 1.4
- MultiQC: summarize analysis results for multiple tools and samples in a single reportBioinformatics201610,041 citationsDataRank 1.4
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 43% comes from its base citations and 57% from the citation network (7 citing papers contributed measurable signal).
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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