Bibliographic record
Abstract
Gene expression can be regulated at the stage of transcription, RNA processing (post- transcriptional changes), and translation. In prokaryotes, the on–off of transcription serves as the main regulatory control of the gene expression whereas, in eukaryotes, more complex regulatory mechanism of transcription takes place. In addition, RNA splicing also plays a major role in the regulation of gene expression. The primary transcript of DNA has complementary sequences of both exons and introns, and is termed heterogeneous RNA (HnRNA). The HnRNA is spliced by the removal of introns and the ligation of exons. The regulation of gene expression in both prokaryotes and eukaryotes is important, as it determines whether a particular protein should be synthesized, and in what quantity. The cells of a multicellular organism are genetically homogeneous, but structurally and functionally heterogeneous, owing to the differential expression of genes. Many of these differences in gene expression arise during development, and are subsequently retained through mitosis. Stable alterations of this type are termed epigenetic. These alterations are heritable in the short term, but do not involve mutations of the DNA itself. The main molecular mechanisms that mediate epigenetic phenomena are DNA methylation and histone modification(s). Keywords: Alternate splicing; Alzheimer's disease; Attenuation; Bromodomain; CAAT box; Chromodomain; Coffin–Lowry syndrome; Cyclic AMP receptor protein (CRP or CAP); Epigenetics; Epigenotype; Epigenetic regulation; Exon; Gratuitous inducer; Intron or intervening sequence; Inducer; Induction; Lariat; Leader sequence; Myoblast; Operator; Polyadenylation; Polycistronic mRNA; Promoter; Regulatory gene; Repression; Rett syndrome; Riboswitch; Ribozyme; RITS (RNA-induced transcriptional silencing); SnRNAs (small nuclear RNAs); SnRNPs; Splicing; TATA box; Telomerase; Telomere; Totipotent; Tropomyosin; Upstream
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".