Transcriptional activation by nuclear receptors is a research paper published in Essays in Biochemistry (2004). On theSindex it has a DataRank of 2.3. It has been cited 59 times, with 51 citing works in its 1-hop citation network.
Transcriptional activation by nuclear receptors (NRs) involves the recruitment of distinct classes of co-activators and other transcription-related factors to target promoters in the chromatin environment of the nucleus. Chromatin has a general repressive effect on transcription, but also provides opportunities for NRs to regulate transcription by directing specific patterns of chromatin remodelling and histone modification. Ultimately, the transcription of hormone-regulated genes by NRs is critically dependent on co-ordinated physical and functional interactions among the receptors, chromatin, co-activators with chromatin-, histone- and factor-modifying activities, and the RNA polymerase II transcriptional machinery. In addition, several mechanisms exist to terminate or attenuate NR-dependent signalling, including modification, recycling, subcellular redistribution and degradation of the receptors or their associated cofactors. The complexity of NR-dependent transcription provides multiple targets for regulatory inputs, thus allowing each hormone-responsive cell to direct its transcriptional output in a physiologically appropriate manner.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.614
From this paper's citation signal
Citation Network Contribution
1.7
From 39 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 27% comes from its base citations and 73% from the citation network (39 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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