Pheno-RNA, a method to associate genes with a specific phenotype, identifies genes linked to cellular transformation is a research paper published in Proceedings of the National Academy of Sciences (2020). On theSindex it has a DataRank of 0.618. It has been cited 10 times, with 10 citing works in its 1-hop citation network.
Significance Pheno-RNA is a new idea/method to identify genes important for a phenotype. It involves 1) generating a phenotypic series that involves different experimental conditions to yield a measurable phenotype, 2) performing transcriptional profiling under all the conditions, and 3) correlating gene expression profiles with phenotypic strength. Using this method, we identified ∼200 genes whose expression profiles over 17 conditions show a remarkably high correlation to the level of transformation. As expected, these ∼200 genes are enriched in biological categories important for transformation. Within these categories, some genes have very high correlations with the transformation phenotype, whereas others do not. Ninety genes with high correlations have not been previously linked to cancer, suggesting heretofore unknown genes with a role in cancer.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.360
From this paper's citation signal
Citation Network Contribution
0.258
From 9 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 58% comes from its base citations and 42% from the citation network (9 citing papers contributed measurable signal).
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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