Bibliographic record
Abstract
The discovery of oncogenes and tumor suppressor genes provided important insights into the molecular pathogenesis of cancer but also revealed some contradictions in the prevailing somatic mutation theory. The evidence that noncoding RNAs (ncRNAs) form an elaborate network that regulates the flow of genetic information in eukaryotic cells offers an explanation for the inconsistencies. ncRNAs comprise a wide variety of molecules that interact with one another as well as with other RNAs, DNA, and proteins, over whose activities they exert a regulatory influence. The outcome of the dynamic interactions of the cell's biomolecules is the emergence of higher-order states of equilibrium, called attractor states, which correspond to the gene-expression configurations of distinct cell types. Attractor states are relatively stable systems, but they are susceptible to perturbation by a disturbing force, such as mutation. Mutations that disrupt the ncRNA network can enable the cell to undergo a state transition towards a potentially neoplastic one. This is the crux of tumorigenesis. An inquiry into the architecture of the ncRNA network and its role in tumorigenesis is required to complement our knowledge of the well-known cancer genes as well as serve as a guide in the design of new anticancer therapeutics.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".