Bibliographic record
Abstract
Mol Syst Biol. 6: 6 Since the discovery of the genetic code by Holley, Khorana, and Nirenberg, researchers have been fascinated by the apparent redundancy of synonymous codons that code for the same amino acid. Subsequent work led to the discovery that synonymous codons are not used randomly in coding sequences. Rather, classes of genes that are characterized by a specific codon usage bias can be identified. Thus, ‘optimal codons’ were defined as codons that are enriched in highly expressed genes. Based on this notion, bioinformatics researchers have developed the Codon Adaptation Index, which has been adopted as an approximate measure for overall gene expression level. The classical theory of codon bias posits that optimal codons correlate with the abundances of cognate tRNAs, and thereby increase translational efficiency and accuracy (Ikemura, 1981) by reducing the rate of ribosomal stalling and decreasing the chances of incorporating the wrong amino acid. Consequently, optimal codons are thought to prevent mistranslation‐induced misfolding (Drummond and Wilke, 2008). In a recent study published in Molecular Systems Biology , Warnecke and Hurst (2010) report evidence for a novel mechanism underlying optimal codon usage. They hypothesize a novel link between codon bias and the cellular folding machinery; namely, the GroEL chaperonin complex. Specifically, proteins that only sporadically rely on chaperones (in trans ) to prevent misfolding are …
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".