Interaction Networks of the Molecular Machines That Decode, Replicate, and Maintain the Integrity of the Human Genome
Bibliographic record
Abstract
The interaction of many proteins with genomic DNA is required for the expression, replication, and maintenance of the integrity of mammalian genomes. These proteins participate in processes as diverse as gene transcription and mRNA processing, as well as in DNA replication, recombination, and repair. This intricate system, where the various nuclear machineries interact with one another and bind to either common or distinct DNA regions to create an impressive network of protein-protein and protein-DNA interactions, is made even more complex by the need for a very stringent control in order to ensure normal cell growth and differentiation. A general methodology based on the in vivo pull-down of tagged components of nuclear machines and regulatory proteins was used to study genome-wide protein-protein and protein-DNA interactions in mammalian cells. In particular, this approach has been used in defining the interaction networks (or "interactome") formed by RNA polymerase II, a molecular machine that decodes the human genome. In addition, because this methodology allows for the purification of variant forms of tagged complexes having site-directed mutations in key elements, it can also be used for deciphering the relationship between the structure and the function of the molecular machines, such as RNA polymerase II, that by binding DNA play a central role in the pathway from the genome to the organism.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".