Bibliographic record
Abstract
DNA repair systems are essential for the maintenance of genome integrity. Consequently, the disregulation of repair genes can be expected to be associated with significant, detrimental health effects, which can include an increased prevalence of birth defects, an enhancement of cancer risk, and an accelerated rate of aging. Although original insights into DNA repair and the genes responsible were largely derived from studies in bacteria and yeast, well over 125 genes directly involved in DNA repair have now been identified in humans, and their cDNA sequence established. These genes function in a diverse set of pathways that involve the recognition and removal of DNA lesions, tolerance to DNA damage, and protection from errors of incorporation made during DNA replication or DNA repair. Additional genes indirectly affect DNA repair, by regulating the cell cycle, ostensibly to provide an opportunity for repair or to direct the cell to apoptosis. For about 70 of the DNA repair genes listed in Table I, both the genomic DNA sequence and the cDNA sequence and chromosomal location have been elucidated. In 45 cases single-nucleotide polymorphisms have been identified and, in some cases, genetic variants have been associated with specific disorders. With the accelerating rate of gene discovery, the number of identified DNA repair genes and sequence variants is quickly rising. This report tabulates the current status of what is known about these genes. The report is limited to genes whose function is directly related to DNA repair.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.021 |
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".